Skip to main content Accessibility help
×
Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-26T00:33:44.767Z Has data issue: false hasContentIssue false

References

Published online by Cambridge University Press:  27 December 2017

Douglas Maraun
Affiliation:
Karl-Franzens-Universität Graz, Austria
Martin Widmann
Affiliation:
University of Birmingham
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2018

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, P., Eitland, E., Hewitson, B., Vaughan, C., Wilby, R. and Zebiak, S. (2015), ‘Toward an ethical framework for climate services. A white paper of the climate services partnership working group on climate services ethics’.
Addor, N., Rohrer, M., Furrer, R. and Seibert, J. (2016), ‘Propagation of biases in climate models from the synoptic to the regional scale: Implications for bias adjustment’, J. Geophys. Res. 121(5), 2075–2089.Google Scholar
Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P.-P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Suesskind, J., Arkin, P. and Nelkin, E. (2003), ‘The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present)’, J. Hydrometeorol. 4(6), 1147–1167.2.0.CO;2>CrossRefGoogle Scholar
Aguilar, E., Auer, I., Brunet, M., Peterson, T. C. and Wieringa, J. (2003), ‘Guidelines on climate metadata and homogenization’.WMO-TD No. 1186. WorldMeteorological Organization, Geneva, Switzerland, 2003.
Akaike, H. (1973), “Information theory and an extension of the maximum likelihood principle”, in B. N., Petrov and F., Csaki, eds., 2nd International Symposium on Information Theory, Budapest, pp. 267–281.Google Scholar
Allcroft, D. J. and Glasbey, C. A. (2003), ‘A latent Gaussian Markov rand om-field model for spatiotemporal rainfall disaggregation’, J. Roy. Stat. Soc. C. 52(4), 487–498.Google Scholar
Anagnostopoulos, G. G., Koutsoyiannis, D., Christofides, A., Efstratiadis, A. and Mamassis, N. (2010), ‘A comparison of local and aggregated climate model outputs with observed data’, Hydrol. Sci. J. 55(7), 1094–1110.CrossRefGoogle Scholar
Arakawa, A. and Schubert, W. H. (1974), ‘Interaction of a cumulus cloud ensemble with the large-scale environment, part i’, J. Atmos. Sci. 31(3), 674–701.2.0.CO;2>CrossRefGoogle Scholar
Auer, I., Böhm, R., Jurkovic, A., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Brunetti, M., Nanni, T., Maugeri, M., Briffa, K., Jones, P., Efthymiadis, D., Mestre, O., Moisselin, J.-M., Begert, M., Brazdil, R., Bochnicek, O., Cegnar, T., Gajic-Capka, M., Zaninovic, K., Mahstorovic, Z., Szalai, S., Szentimrey, T. and Mercalli, L. (2005), ‘A new instrumental precipitation dataset for the greater alpine region for the period 1800–2002’, Int. J. Climatol. 25(2), 139–166.CrossRefGoogle Scholar
Austin, P. M. (1987), ‘Relation between measured radar reflectivity and surface rainfall’, Mon. Wea. Rev. 115(5), 1053–1070.2.0.CO;2>CrossRefGoogle Scholar
Bagrov, N. A. (1959), ‘Analytic representation of a sequence of meteorological fields via natural orthogonal components’, Trudy Tsentr. Inst. Progn 74, 3–24.Google Scholar
Baldwin, M. P. and Dunkerton, T. J. (2001), ‘Stratospheric harbingers of anomalous weather regimes’, Science 294(5542), 581–584.CrossRefGoogle ScholarPubMed
Bardossy, A., Bogardi, I. and Matyasovszky, I. (2005), ‘Fuzzy rule-based downscaling of precipitation’, Theor. Appl. Climatol. 82(1), 119–129.CrossRefGoogle Scholar
Bárdossy, A. and Pegram, G. G.S. (2009), ‘Copula based multisite model for daily precipitation simulation’, Hydrol. Earth Syst. Sci. 13(12), 2299.CrossRefGoogle Scholar
Bárdossy, A. and Plate, E. J. (1991), ‘Modeling daily rainfall using a semi-Markov representation of circulation pattern occurrence’, J. Hydrol. 122, 33–47.CrossRefGoogle Scholar
Bárdossy, A. and Plate, E. J. (1992), ‘Space-time model for daily rainfall using atmospheric circulation patterns’, Wat. Resour. Res. 28, 1247–1259.CrossRefGoogle Scholar
Barkhordarian, A., von Storch, H. and Bhend, J. (2013), ‘The expectation of future precipitation change over the Mediterranean region is different from what we observe’, Clim. Dynam. 40(1–2), 225–244.CrossRefGoogle Scholar
Barnett, T. P. and Preisendorfer, R. W. (1978), ‘Multifield analog prediction of short-term climate fluctuations using a climate state vector’, J. Atmos. Sci. 35(10), 1771–1787.2.0.CO;2>CrossRefGoogle Scholar
Barry, R. G. (2008), Mountain weather and climate, Cambridge University Press.CrossRefGoogle Scholar
Barry, R. G. and Blanken, P. D. (2016), Microclimate and local climate, Cambridge University Press.CrossRefGoogle Scholar
Barry, R. G. and Chorley, R. J. (2009), Atmosphere, weather and climate, Routledge.Google ScholarPubMed
Barsugli, J. J., Guentchev, G., Horton, R. M., Wood, A., Mearns, L. O., Liang, X.-Z., Winkler, J. A., Dixon, K., Hayhoe, K., Rood, R. B., Goddard, L., Ray, A., Buja, L. and Ammann, C. (2013), ‘The practitioner's dilemma: How to assess the credibility of downscaled climate projections’, EOS 94(46), 424–425.CrossRefGoogle Scholar
Bartholy, J., Bogardi, I. and Matyasovszky, I. (1995), ‘Effect of climate change on regional precipitation in Lake Balaton watershed’, Theor. Appl. Climatol. 51(4), 237–250.CrossRefGoogle Scholar
Bates, B. C., Charles, S. P. and Hughes, J. P. (1998), ‘Stochastic downscaling of numerical climate model simulations’, Env. Mod. Soft. 13(3), 325–331.Google Scholar
Bechtold, P. (2015), ‘Atmospheric moist convection. ECMWF Lecture Notes’.
Becker, N., Ulbrich, U. and Klein, R. (2015), ‘Systematic large-scale secondary circulations in a regional climate model’, Geophys. Res. Lett. 42(10), 4142–4149.CrossRefGoogle Scholar
Beckmann, B.-R. and Buishand, T. A. (2002), ‘Statistical downscaling relationships for precipitation in the Netherland s and North Germany’, Int. J. Climatol. 22(1), 15–32.CrossRefGoogle Scholar
Beersma, J. J. and Buishand, T. A. (2003), ‘Multi-site simulation of daily precipitation and temperature conditional on the atmospheric circulation’, Clim. Res. 25, 121–133.CrossRefGoogle Scholar
Befort, D. J., Wild, S., Kruschke, T., Ulbrich, U. and Leckebusch, G. C. (2016), ‘Different longterm trends of extra-tropical cyclones and windstorms in ERA-20C and NOAA-20CR reanalyses’, Atmos. Sci. Lett. 17(11), 586–595.CrossRefGoogle Scholar
Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. and Vialard, J. (2014), ‘ENSO representation in climate models: from CMIP3 to CMIP5’, Clim. Dynam. 42, 1999–2018.CrossRefGoogle Scholar
Bellone, E., Hughes, J. P. and Guttorp, P. (2000), ‘A hidden Markov model for downscaling synoptic atmospheric patterns to precipitation amounts’, Clim. Res. 15(1), 1–12.CrossRefGoogle Scholar
Bellprat, O., Kotlarski, S., Lüthi, D. and Schär, C. (2013), ‘Physical constraints for temperature biases in climate models’, Geophys. Res. Lett. 40, 4042–4047.CrossRefGoogle Scholar
Beltrami, E. (1873), ‘Sulle funzioni bilineari’, Giornale di Matematiche ad Uso degli Studenti Delle Universita 11(2), 98–106.Google Scholar
Benestad, R. E. (2001), ‘A comparison between two empirical downscaling strategies’, Int. J. Climatol. 21(13), 1645–1668.CrossRefGoogle Scholar
Benestad, R. E. (2002), ‘Empirically downscaled temperature scenarios for northern Europe based on a multi-model ensemble’, Clim. Res. 21(2), 105–125.CrossRefGoogle Scholar
Benestad, R. E. (2005), ‘Climate change scenarios for northern Europe from multi-model IPCC AR4 climate simulations’, Geophys. Res. Lett. 32(17).CrossRefGoogle Scholar
Benestad, R. E. (2011), ‘A new global set of downscaled temperature scenarios’, J. Climate 24(8), 2080–2098.CrossRefGoogle Scholar
Benestad, R. E., Chen, D., Mezghani, A., Fan, L. and Parding, K. (2015), ‘On using principal components to represent stations in empirical-statistical downscaling’, Tellus A 67, 28326.CrossRefGoogle Scholar
Benestad, R. E., Hanssen-Bauer, I. and Chen, D. (2008), Empirical-statistical downscaling, World Scientific Publishing Co Inc.CrossRefGoogle Scholar
Benestad, R. E., Hanssen-Bauer, I. and Førland, E.J. (2007), ‘An evaluation of statistical models for downscaling precipitation and their ability to capture long-term trends’, Int. J. Climatol. 27(5), 649–665.CrossRefGoogle Scholar
Bengtsson, L., Arkin, P., Berrisford, P., Bougeault, P., Folland, C. K., Gordon, C., Haines, K., Hodges, K. I., Jones, P., Kallberg, P., Rayner, N., Simmons, A. J., Stammer, D., Thorne, P. W., Uppala, S. and Vose, R. S. (2007), ‘The need for a dynamical climate reanalysis’, Bull. Amer. Meteorol. Soc. 88(4), 495–501.CrossRefGoogle Scholar
Bengtsson, L., Hagemann, S. and Hodges, K. I. (2004), ‘Can climate trends be calculated from reanalysis data?’, J. Geophys. Res. 109, D11111.CrossRefGoogle Scholar
Berger, A. (1988), ‘Milankovitch theory and climate’, Rev. Geophys. 26(4), 624–657.CrossRefGoogle Scholar
Betts, A. K. and Miller, M. J. (1986), ‘A new convective adjustment scheme. Part II: Single column tests using GATE wave, BOMEX, ATEX and arctic air-mass data sets’, Quart. J. Roy. Meteorol. Soc. 112(473), 693–709.Google Scholar
Bevacqua, E., Maraun, D., Hobæk Haff, I., Widmann, M. and Vrac, M. (2017), ‘Multivariate Statistical Modelling of Compound Events via Pair-Copula Constructions: Analysis of Floods in Ravenna’, Hydrol. Earth Syst. Sci. 21(6), 2701–2723.CrossRefGoogle Scholar
Bhend, J. and Whetton, P. (2013), ‘Consistency of simulated and observed regional changes in temperature, sea level pressure and precipitation’, Clim. Change 118(3–4), 799–810.CrossRefGoogle Scholar
Boberg, F. and Christensen, J. H. (2012), ‘Overestimation of Mediterranean summer temperature projections due to model deficiencies’, Nat. Clim. Change 2(6), 433–436.CrossRefGoogle Scholar
Boé, J., Terray, L., Habets, F. and Martin, E. (2007), ‘Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies’, Int. J. Climatol. 27, 1643–1655.CrossRefGoogle Scholar
Böhm, R., Auer, I., Brunetti, M., Maugeri, M., Nanni, T. and Schöner, W. (2001), ‘Regional temperature variability in the European Alps: 1760–1998 from homogenized instrumental time series’, Int. J. Climatol. 21(14), 1779–1801.CrossRefGoogle Scholar
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S., Dufresne, J.-L., Hall, A., Hallegatte, S., Holland, M. M., Ingram, W., Rand all, D. A., Soden, B. J., Tselioudis, G. and Webb, M. J. (2006), ‘How well do we understand and evaluate climate change feedback processes?’, J. Climate 19(15), 3445–3482.CrossRefGoogle Scholar
Bony, S., Stevens, B., Frierson, D. M.W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M. and Webb, M. J. (2015), ‘Clouds, circulation and climate sensitivity’, Nat. Geosci. 8(4), 261–268.CrossRefGoogle Scholar
Bothe, O., Evans, M., Fernández Donado, L., Garcia Bustamante, E., Gergis, J., Gonzalez-Rouco, J.F., Goosse, H., Hegerl, G., Hind, A., Jungclaus, J. H. et al. (2015), ‘Continental-scale temperature variability in PMIP3 simulations and PAGES 2k regional temperature reconstructions over the past millennium’, Clim. Past 11, 1673–1699.Google Scholar
Boucher, O., Rand all, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B. and Zhang, X. Y. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Clouds and Aerosols’.Google Scholar
Boulanger, J., Brasseur, G., Carril, A. F., de Castro, M., Degallier, N., Ereño, C., Le Treut, H., Marengo, J. A., Menendez, C. G., Nuñez, M.N., Penalba, O. C., Rolla, A. L., Rusticucci, M. and Terra, R. (2010), ‘A Europe–South America network for climate change assessment and impact studies’, Clim. Change 98(3), 307–329.CrossRefGoogle Scholar
Box, G. E. P. and Draper, N. R. (1987), Empirical model-building and response surfaces, Vol. 424, Wiley New York.Google Scholar
Brand s, S., Gutiérrez, J.M. and Herrera, S. (2012), ‘On the use of reanalysis data for downscaling’, J. Climate 25, 2517–2526.Google Scholar
Brand sma, T. and Van der Meulen, J. P. (2008), ‘Thermometer screen intercomparison in De Bilt (the Netherland s) Part II: Description and modeling of mean temperature differences and extremes’, Int. J. Climatol. 28(3), 389–400.Google Scholar
Brayshaw, D. J., Hoskins, B. and Blackburn, M. (2015), ‘The basic ingredients of the North Atlantic storm track. part I: Land –sea contrast and orography’, J. Atmos. Sci. 72(9).Google Scholar
Bretherton, C. S., Smith, C. and Wallace, J. M. (1992), ‘An intercomparison ofmethods for finding coupled patterns in climate data’, J. Climate 5(6), 541–560.2.0.CO;2>CrossRefGoogle Scholar
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F.B. and Jones, P. D. (2006), ‘Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850’, J. Geophys. Res. 111, D12106.CrossRefGoogle Scholar
Brown, C., Ghile, Y., Laverty, M. and Li, K. (2012), ‘Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector’, Wat. Resour. Res. 48, W09537.CrossRefGoogle Scholar
Brown, C. and Wilby, R. L. (2012), ‘An alternate approach to assessing climate risks’, EOS 93(41), 401–402.CrossRefGoogle Scholar
Buell, C. E. (1975), The topography of empirical orthogonal functions, in ‘Preprints Fourth Conference on Probability and Statistics in Atmospheric Science’, p. 188.
Buell, C. E. (1979), On the physical interpretation of empirical orthogonal functions, in ‘Preprints Sixth Conference on Probability and Statistics in Atmospheric Science’, p. 112.
Buishand, T. A. (1977), Stochastic modeling of daily rainfall sequences., Technical Report 77-3.
Buishand, T. A. and Brand sma, T. (2001), ‘Multisite simulation of daily precipitation and temperature in the Rhine basin by nearest-neighbor resampling’, Wat. Resour. Res. 37(11), 2761–2776.CrossRefGoogle Scholar
Bukovsky, M. S. (2012), ‘Temperature trends in the NARCCAP regional climate models’, J. Climate 24, 3985–3991.Google Scholar
Bukovsky, M. S. and Karoly, D. J. (2009), ‘Precipitation simulations using WRF as a nested regional climate model’, J. Appl. Meteorol. Climatol. 48(10), 2152–2159.CrossRefGoogle Scholar
Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R. and Cannon, A. J. (2012), ‘Downscaling extremes - an intercomparison of multiple statistical methods for present climate’, J. Climate 25, 4366–4388.CrossRefGoogle Scholar
Buser, C. M., Künsch, H.R., Lüthi, D., Wild, M. and Schär, C. (2009), ‘Bayesian multi-model projection of climate: bias assumptions and interannual variability’, Clim. Dynam. 33, 849– 868.CrossRefGoogle Scholar
Busuioc, A., von Storch, H. and Schnur, R. (1999), ‘Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions’, J. Climate 12, 258–272.CrossRefGoogle Scholar
Butler, A. H., Thompson, D. W.J. and Heikes, R. (2010), ‘The steady-state atmospheric circulation response to climate change - like thermal forcings in a simple general circulation model’, J. Climate 23(13), 3474–3496.CrossRefGoogle Scholar
Cabré, M. F., Solman, S. A. and Nuñez, M.N. (2010), ‘Creating regional climate change scenarios over southern South America for the 2020's and 2050's using the pattern scaling technique: validity and limitations’, Clim. Change 98(3–4), 449–469.CrossRefGoogle Scholar
Caldwell, P., Chin, H.-N. S., Bader, D. C. and Bala, G. (2009), ‘Evaluation of a WRF dynamical downscaling simulation over California’, Clim. Change 95, 499–521.CrossRefGoogle Scholar
Cannon, A. J. (2011), ‘Quantile regression neural networks: Implementation in R and application to precipitation downscaling’, Comp. Geosci. 37(9), 1277–1284.CrossRefGoogle Scholar
Cannon, A. J. (2016), ‘Multivariate Bias Correction of ClimateModel Output:MatchingMarginal Distributions and Intervariable Dependence Structure’, J. Climate 29(19), 7045–7064.CrossRefGoogle Scholar
Cannon, A. J. (2017), ‘Multivariate quantile mapping bias correction: an n-dimensional probability density function transform for climate model simulations of multiple variables’, Clim. Dynam. pp. 1–19, DOI: 10.1007/s00382-017-3580-6.CrossRef
Casanueva, A., Frías, M.D., Herrera, S., San-Martín, D., Zaninovic, K. and Gutiérrez, J.M. (2014), ‘Statistical downscaling of climate impact indices: testing the direct approach’, Clim. Change 127(3–4), 547–560.CrossRefGoogle Scholar
Cash, D., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N. and Jäger, J. (2002), ‘Salience, credibility, legitimacy and boundaries: linking research, assessment and decision making’, John F. Kennedy School of Government, Harvard University, Faculty ResearchWorking Papers Series.
Casola, J. H. and Wallace, J. M. (2007), ‘Identifying weather regimes in the wintertime 500- hPa geopotential height field for the Pacific–North American sector using a limited-contour clustering technique’, J. Appl. Meteorol. Climatol. 46(10), 1619–1630.CrossRefGoogle Scholar
Castro, C. L., Pielke, R. A. and Leoncini, G. (2005), ‘Dynamical downscaling: Assessment of value retained and added using the Regional Atmospheric Modeling System (RAMS)’, J. Geophys. Res. 110(D5), D05108.CrossRefGoogle Scholar
Caussinus, H. and Mestre, O. (2004), ‘Detection and correction of artificial shifts in climate series’, J. Roy. Stat. Soc. C 53(3), 405–425.Google Scholar
Cavazos, T. and Hewitson, B. C. (2005), ‘Performance of NCEP-NCAR reanalysis variables in statistical downscaling of daily precipitation’, Clim. Res. 28, 95–107.Google Scholar
Cayan, D., Kunkel, K., Castro, C., Gershunov, A., Barsugli, J., Ray, A., Overpeck, J., Anderson, M., Russell, J., Rajagopalan, B., Rangwala, I. and Duffy, P. (2013), Assessment of Climate Change in the Southwest United States: A Report Prepared for the National Climate Assessment, Island Press, chapter ‘Future climate: Projected average’, pp. 101–125.Google Scholar
Ceppi, P., Scherrer, S. C., Fischer, A. M. and Appenzeller, C. (2012), ‘Revisiting Swiss temperature trends 1959–2008’, Int. J. Climatol. 32, 203–213.CrossRefGoogle Scholar
Chand ler, R. E. (2005), ‘On the use of generalized linear models for interpreting climate variability’, Environmetrics 16, 699–715.Google Scholar
Chand ler, R. E. (2013), ‘Exploiting strength, discounting weakness: combining information from multiple climate simulators’, Phil. Trans. R. Soc. A 371(1991), 20120388.Google Scholar
Chand ler, R. E. and Bate, S. (2007), ‘Inference for clustered data using the independence loglikelihood’, Biometrika 94, 167–183.Google Scholar
Chand ler, R. E. and Wheater, H. S. (2002), ‘Analysis of rainfall variability using generalized linear models: A case study from the west of Ireland’, Wat. Resour. Res. 38(10), 1192.Google Scholar
Charles, S. P., Bari, M. A., Kitsios, A. and Bates, B. C. (2007), ‘Effect of GCM bias on downscaled precipitation and runoff projections for the Serpentine catchment, Western Australia’, Int. J. Climatol. 27(12), 1673–1690.CrossRefGoogle Scholar
Charles, S. P., Bates, B. C., Whetton, P. H. and Hughes, J. P. (1999), ‘Validation of downscaling models for changed climate conditions: case study of southwestern Australia’, Clim. Res. 12, 1– 14.CrossRefGoogle Scholar
Chen, L., Li, T. and Yu, Y. (2015), ‘Causes of strengthening and weakening of ENSO amplitude under global warming in four CMIP5 models’, J. Climate 28, 3250–3274.CrossRefGoogle Scholar
Christensen, J. H., Boberg, F., Christensen, O. B. and Lucas-Picher, P. (2008), ‘On the need for bias correction of regional climate change projections of temperature and precipitation’, Geophys. Res. Lett. 35, L20709.CrossRefGoogle Scholar
Christensen, J. H. and Christensen, O. B. (2007), ‘A summary of the PRUDENCE model projections of changes in European climate by the end of this century’, Clim. Change 81, 7–30.CrossRefGoogle Scholar
Christensen, J. H., Hewitson, B., Busuioc, A., Chen, A., Gao, X., Held, I., Jones, R., Kolli, R. K., Kwon, W.-T., Laprise, R., Rueda, V. M., Mearns, L., Menéndez, C.G., Räisänen, J., Rinke, A., Sarr, A. and Whetton, P. (2007), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, chapter ‘Regional Climate Projections’.Google Scholar
Christensen, J. H., Machenhauer, B., Jones, R. G., Schär, C., Ruti, P. M., Castro, M. and Visconti, G. (1997), ‘Validation of present-day regional climate simulations over Europe: LAM simulations with observed boundary conditions’, Clim. Dynam. 13, 489–506.CrossRefGoogle Scholar
Chu, J.-L., Kang, H., Tam, C.-Y., Park, C.-K. and Chen, C.-T. (2008), ‘Seasonal forecast for local precipitation over northern taiwan using statistical downscaling’, J. Geophys. Res. 113(D12).CrossRefGoogle Scholar
Chu, J.-L. and Yu, P.-S. (2010), ‘A study of the impact of climate change on local precipitation using statistical downscaling’, J. Geophys. Res. 115, D10105.CrossRefGoogle Scholar
Clark, P. (2009), ‘Issues with high-resolution NWP’, Technical report, United Kingdom Met Office.
Coe, R. and Stern, R. D. (1982), ‘Fitting models to daily rainfall data’, J. Appl. Meteorol. 21(7), 1024–1031.2.0.CO;2>CrossRefGoogle Scholar
Cohen, S. J. and Allsopp, T. R. (1988), ‘The potential impacts of a scenario of CO2-induced climatic change on Ontario, Canada’, J. Climate 1, 669–681.2.0.CO;2>CrossRefGoogle Scholar
Coles, S. (2001), An introduction to statistical modeling of extreme values, Springer Series in Statistics, Springer.
Colette, A., Vautard, R. and Vrac, M. (2012), ‘Regional climate downscaling with prior statistical correction of the global climate forcing’, Geophys. Res. Lett. 39(13), L13707.CrossRefGoogle Scholar
Collins, M., Booth, B. B.B., Harris, G. R., Murphy, J. M., Sexton, D. M.H. and Webb, M. J. (2006), ‘Towards quantifying uncertainty in transient climate change’, Clim. Dynam. 27, 127– 147.CrossRefGoogle Scholar
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A. J. and Wehner, M. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Long-Term Climate Change: Projections, Committments and Irreversibility’.Google Scholar
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, O., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D. and Worley, S. J. (2011), ‘The twentieth century reanalysis project’, Quart. J. Roy. Meteorol. Soc. 137(654), 1–28.CrossRefGoogle Scholar
Cooley, D., Nychka, D. and Naveau, P. (2007), ‘Bayesian spatial modeling of extreme precipitation return levels’, J. Am. Stat. Ass. 102(479), 824–840.CrossRefGoogle Scholar
CORDEX (2016), ‘Bias-adjusted RCM data’, http://www.cordex.org/index.php?option=com_ content & view=article & id=275 & Itemid=785.
Cowpertwait, P., Isham, V. and Onof, C. (2007), ‘Point process models of rainfall: developments for fine-scale structure’, Proc. Roy. Soc. A 463(2086), 2569–2588.CrossRefGoogle Scholar
Cowpertwait, P. S. P. (1994), A generalized point process model for rainfall, in ‘Proc. Roy. Soc. A’, Vol. 447, pp. 23–37.CrossRefGoogle Scholar
Cowpertwait, P. S. P., Kilsby, C. G. and O'Connell, P.E. (2002), ‘A space-time Neyman-Scott model of rainfall: Empirical analysis of extremes’, Wat. Resour. Res. 38(8), 1131.CrossRefGoogle Scholar
Cox, D. R. and Hinkley, D. V. (1994), Theoretical statistics, Chapman & Hall, London.Google Scholar
Cox, D. R. and Isham, V. (1988), A simple spatial-temporal model of rainfall, in ‘Proc. Roy. Soc. A’, Vol. 415, pp. 317–328.CrossRefGoogle Scholar
Cox, D. R. and Isham, V. S. (1994), Statistics for the environment, 2: Water related issues, Wiley, chapter ‘Stochastic Models of Precipitation’, pp. 3–18.
Craddock, J. and Flood, C. (1969), ‘Eigenvectors for representing the 500 mb geopotential surface over the Northern Hemisphere’, Quart. J. Roy. Meteorol. Soc. 95(405), 576–593.CrossRefGoogle Scholar
Crowley, T. (1990), ‘Are there any satisfactory geologic analogs for a future greenhouse warming?’, J. Climate 3, 1282–1292.2.0.CO;2>CrossRefGoogle Scholar
Daly, C., Neilson, R. P. and Phillips, D. L. (1994), ‘A statistical-topographic model for mapping climatological precipitation over mountainous terrain’, J. Appl. Meteorol. 33(2), 140–158.2.0.CO;2>CrossRefGoogle Scholar
Davini, P., von Hardenberg, J., Corti, S., Christensen, H. M., Juricke, S., Subramanian, A., Watson, P. A. G., Weisheimer, A. and Palmer, T. N. (2017), ‘Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model’, Geosci. Model Dev. 10(3), 1383–1402.CrossRefGoogle Scholar
Davison, A. C. (2003), Statistical models, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press.CrossRefGoogle Scholar
Dawson, A., Palmer, T. N. and Corti, S. (2012), ‘Simulating regime structures in weather and climate prediction models’, Geophys. Res. Lett. 39(21).CrossRefGoogle Scholar
Dayon, G., Boé, J. and Martin, E. (2015), ‘Transferability in the future climate of a statistical downscaling method for precipitation in France’, J. Geophys. Res. 120(3), 1023–1043.Google Scholar
De Elia, R. (2014), ‘Specificities of climate modeling research and the challenges in communicating to users’, Bull. Amer. Meteorol. Soc. 95(7), 1003–1010.CrossRefGoogle Scholar
Deardorff, J. W. (1972), ‘Numerical investigation of neutral and unstable planetary boundary layers’, J. Atmos. Sci. 29(1), 91–115.2.0.CO;2>CrossRefGoogle Scholar
Dee, D. P., Källén, E., Simmons, A. J. and Haimberger, L. (2011 a), ‘Comments on “Reanalyses suitable for characterizing long-term trends”’, Bull. Amer. Meteorol. Soc. 92(1), 65–70.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beeljars, A. C.M., van den Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N. and Vitart, F. (2011b), ‘The ERA-Interim reanalysis: configuration and performance of the data assimilation system’, Quart. J. Roy. Meteorol. Soc. 137, 553–597.CrossRefGoogle Scholar
Deidda, R., Badas, M. G. and Piga, E. (2006), ‘Space-time multifractality of remotely sensed rainfall fields’, J. Hydrol. 322(1), 2–13.CrossRefGoogle Scholar
Della-Marta, P. M. and Wanner, H. (2006), ‘A method of homogenizing the extremes and mean of daily temperature measurements’, J. Climate 19(17), 4179–4197.CrossRefGoogle Scholar
Denis, B., Laprise, R., Caya, D. and Côté, J. (2002), ‘Downscaling ability of one-way nested regional climate models: the Big-Brother Experiment’, Clim. Dynam. 18(8), 627–646.Google Scholar
Déqué, M., Rowell, D. P., Luthi, D., Giorgi, F., Christensen, J. H., Rockel, B., Jacob, D., Kjellström, E., de Castro, M. and van den Hurk, B. (2007), ‘An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections’, Clim. Change 81, 53–70.CrossRefGoogle Scholar
Deser, C., Knutti, R., Solomon, S. and Phillips, A. S. (2012), ‘Communication of the role of natural variability in future North American climate’, Nat. Clim. Change 2, 775–779.Google Scholar
Deser, F., Rockel, B., von Storch, H., Winterfeldt, J. and Zahn, M. (2011), ‘Regional climate models add value to global model data. a review and selected examples’, Bull. Amer. Meteorol. Soc. 92, 1181–1192.Google Scholar
Dessai, S. (2009), ‘Do we need better predictions to adapt to a changing climate?’, EOS 90, 111–112.CrossRefGoogle Scholar
Dessai, S. and Hulme, M. (2004), ‘Does climate adaptation policy need probabilities?’, Climate Policy 4(2), 107–128.CrossRefGoogle Scholar
Di Luca, A., de Elía, R. and Laprise, R. (2015), ‘Challenges in the quest for added value of regional climate dynamical downscaling’, Curr. Clim. Change Rep. 1(1), 10–21.CrossRefGoogle Scholar
Dickinson, R. E., Errico, R. M., Giorgi, F. and Bates, G. T. (1989), ‘A regional climate model for the Western United States’, Clim. Change 15, 383–422.CrossRefGoogle Scholar
Diggle, P. J. and Ribeiro, P. J. (2007), Model-based geostatistics, Springer Series in statistics, Springer.Google Scholar
Ding, H., Keenlyside, N., Latif, M., Park, W. and Wahl, S. (2015), ‘The impact of mean state errors on equatorial atlantic interannual variability in a climate model’, J. Geophys. Res. 120(2), 1133–1151.CrossRefGoogle Scholar
Director, H. and Bornn, L. (2015), ‘Connecting point-level and gridded moments in the analysis of climate data’, J. Climate 28(9), 3496–3510.CrossRefGoogle Scholar
Dobson, A. J. (2001), An introduction to generalized linear models, Chapman and Hall, London.CrossRefGoogle Scholar
Dommenget, D. and Latif, M. (2002), ‘A cautionary note on the interpretation of EOFs’, J. Clim. 15(2), 216–225.2.0.CO;2>CrossRefGoogle Scholar
Döscher, R., Willén, U., Jones, C., Rutgersson, A., Meier, H. E.M., Hansson, U. and Graham, L. P. (2002), ‘The development of the regional coupled ocean-atmosphere model RCAO’, Boreal Env. Res. 7(3), 183–192.Google Scholar
Dosio, A. (2016), ‘Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution EURO-CORDEX regional climate models’, J. Geophys. Res. 121(10), 5488–5511.Google Scholar
Dosio, A., Paruolo, P. and Rojas, R. (2012), ‘Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: Analysis of the climate change signal’, J. Geophys. Res. Atmos. 117, D17110.CrossRefGoogle Scholar
Dubrovsky, M. (1997), ‘Creating daily weather series with use of the weather generator’, Environmetrics 8(5), 409–424.3.0.CO;2-0>CrossRefGoogle Scholar
Dubrovský, M., Buchtele, J. and Žalud, Z. (2004), ‘High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling’, Clim. Change 63(1), 145–179.CrossRefGoogle Scholar
Dufresne, J. L. and Bony, S. (2008), ‘An assessment of the primary sources of spread of global warming estimates from coupled atmosphere-ocean models’, J. Climate 21, 5135–5144.CrossRefGoogle Scholar
Dunn, P. K. (2004), ‘Occurrence and quantity of precipitation can be modelled simultaneously’, Int. J. Climatol. 24(10), 1231–1239.CrossRefGoogle Scholar
Easterling, D. R. (1999), ‘Development of regional climate scenarios using a downscaling approach’, Clim. Change 41(3–4), 615–634.CrossRefGoogle Scholar
Eckart, C. and Young, G. (1936), ‘The approximation of one matrix by another of lower rank’, Psychometrika 1(3), 211–218.CrossRefGoogle Scholar
Eckart, C. and Young, G. (1939), ‘A principal axis transformation for non-hermitian matrices’, Bull. Amer. Math. Soc. 45(2), 118–121.CrossRefGoogle Scholar
ECMWF (2016), IFS documentation – Cy41r2; operational implementation 8 March 2016, ECMWF, chapter ‘Part IV: Physical Processes’.
Eden, C., Greatbatch, R. J. and Böning, C.W. (2004), ‘Adiabatically correcting an eddypermitting model using large-scale hydrographic data: Application to the Gulf Stream and the North Atlantic Current’, J. Phys. Oceanogr. 34(4), 701–719.2.0.CO;2>CrossRefGoogle Scholar
Eden, J. M., Widmann, M., Maraun, D. and Vrac, M. (2014), ‘Comparison of GCM-and RCMsimulated precipitation following stochastic postprocessing’, J. Geophys. Res. 119(19).Google Scholar
Eden, J., Widmann, M., Grawe, D. and Rast, S. (2012), ‘Skill, correction, and downscaling of GCM-simulated precipitation’, J. Climate 25, 3970–3984.CrossRefGoogle Scholar
Edwards, P. N. (2011), ‘History of climate modeling’, WIRES Clim. Change 2(1), 128–139.CrossRefGoogle Scholar
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K. and Liebert, J. (2012), ‘Should we apply bias correction to global and regional climate model data?’, Hydrol. Earth Syst. Sci. 16, 3391– 3404.CrossRefGoogle Scholar
Eitzinger, J. and Thaler, S. (2016), ‘STARC-Impact Kick-Off Meeting. Introduction toWorkpackage 4’.
Embrechts, P., Klüppelberg, C. and Mikosch, T. (1997), Modelling extremal events for insurance and finance, Applications in Mathematics, Springer.CrossRefGoogle Scholar
Engen-Skaugen, T. (2007), ‘Refinement of dynamically downscaled precipitation and temperature scenarios’, Clim. Change 84(3–4), 365–382.CrossRefGoogle Scholar
Entekhabi, D., Rodriguez-Iturbe, I. and Eagleson, P. S. (1989), ‘Probabilistic representation of the temporal rainfall process by a modified Neyman-Scott Rectangular Pulses Model: Parameter estimation and validation’, Wat. Resour. Res. 25(2), 295–302.CrossRefGoogle Scholar
European Commission (2013), ‘An EU Strategy on adaptation to climate change’, http://ec.europa.eu/clima/policies/adaptation.
Farmer, S. (1971), ‘An investigation into the results of principal component analysis of data derived from rand om numbers’, The Statistician 20(4), 63–72.CrossRefGoogle Scholar
Fawcett, L. and Walshaw, D. (2007), ‘Improved estimation for temporally clustered extremes’, Environmetrics 18(2), 173–188.CrossRefGoogle Scholar
Ferraris, L., Gabellani, S., Rebora, N. and Provenzale, A. (2003), ‘A comparison of stochastic models for spatial rainfall downscaling’, Wat. Resour. Res. 39(12), 1368.CrossRefGoogle Scholar
Fischer, E. M., Seneviratne, S. I., Vidale, P. L., Lüthi, D. and Schär, C. (2007), ‘Soil moistureatmosphere interactions during the 2003 European summer heat wave’, J. Climate 20, 5081– 5099.CrossRefGoogle Scholar
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C. and Rummukainen, M. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Evaluation of Climate Models’.Google Scholar
Foley, A. M. (2010), ‘Uncertainty in regional climate modelling: A review’, Prog. Phys. Geogr. 34(5), 647–670.CrossRefGoogle Scholar
Fowler, H. J., Blenkinsop, S. and Tebaldi, C. (2007), ‘Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling’, Int. J. Climatol. 27, 1547–1578.CrossRefGoogle Scholar
Fowler, H. J., Kilsby, C. G. and O'Connell, P.E. (2000), ‘A stochastic rainfall model for the assessment of regionl water resources systems under changed climate conditions’, Hydrol. Earth. Syst. Sci. 4(2), 263–282.CrossRefGoogle Scholar
Fraley, C. and Raftery, A. E. (2002), ‘Model-based clustering, discriminant analysis, and density estimation’, J. Amer. Stat. Assoc. 97(458), 611–631.CrossRefGoogle Scholar
Fraley, C. and Raftery, A. E. (2007), ‘Model-based methods of classification: using the mclust software in chemometrics’, J. Stat. Soft. 18(6), 1–13.CrossRefGoogle Scholar
Frei, C., Christensen, J. H., Deque, M., Jacob, D., Jones, R. G. and Vidale, P. L. (2003), ‘Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps’, J. Geophys. Res. 108(D3), 4124.CrossRefGoogle Scholar
Frei, C. and Schär, C. (1998), ‘A precipitation climatology of the Alps from high-resolution raingauge observations’, Int. J. Climtol. 18(8), 873–900.Google Scholar
Frei, C., Schöll, R., Fukutome, S., Schmidli, J. and Vidale, P. L. (2006), ‘Future change of precipitation extremes in Europe: an intercomparison of scenarios from regional climate models’, J. Geophys. Res. 111, D06105.CrossRefGoogle Scholar
Frey-Buness, F., Heimann, D. and Sausen, R. (1995), ‘A statistical-dynamical downscaling procedure for global climate simulations’, Theor. Appl. Climatol. 50(3–4), 117–131.CrossRefGoogle Scholar
Frías, M. D., Zorita, E., Fernández, J. and Rodríguez-Puebla, C. (2006), ‘Testing statistical downscaling methods in simulated climates’, Geophys. Res. Lett. 33, L19807.CrossRefGoogle Scholar
Friederichs, P. and Hense, A. (2007), ‘Statistical downscaling of extreme precipitation events using censored quantile regression’, Mon. Wea. Rev. 135(6), 2365–2378.CrossRefGoogle Scholar
Fritsch, J. M., Chappell, C. F. and Hoxit, L. R. (1976), ‘The use of large-scale budgets for convective parameterization’, Mon. Wea. Rev. 104(11), 1408–1418.Google Scholar
Frost, A. J., Charles, S. P., Timbal, B., Chiew, F. H.S., Mehrotra, R., Nguyen, K. C., Chand ler, R. E., McGregor, J. L., Fu, G., Kirono, D. G.C. et al. (2011), ‘A comparison of multisite daily rainfall downscaling techniques under Australian conditions’, J. Hydrol. 408(1), 1–18.CrossRefGoogle Scholar
Fuentes, U. and Heimann, D. (2000), ‘An improved statistical-dynamical downscaling scheme and its application to the Alpine precipitation climatology’, Theor. Appl. Climatol. 65(3–4), 119– 135.CrossRefGoogle Scholar
Funk, C., Peterson, P., Land sfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A. and Michaelsen, J. (2015), ‘The climate hazards infrared precipitation with stations - a new environmental record for monitoring extremes’, Scientific Data 2, 150066.CrossRefGoogle ScholarPubMed
Gabriel, K. R. and Neumann, J. (1962), ‘A Markov chain model for daily rainfall occurrence at Tel Aviv’, Quart. J. Roy. Meteorol. Soc. 88, 90–95.CrossRefGoogle Scholar
Gangopadhyay, S., Pruitt, T., Brekke, L. and Raff, D. (2011), ‘Hydrologic projections for the Western United States’, EOS 92(48), 441–442.CrossRefGoogle Scholar
García-Morales, M. B. and Dubus, L. (2007), ‘Forecasting precipitation for hydroelectric power management: how to exploit GCM's seasonal ensemble forecasts’, Int. J. Climatol. 27(12), 1691–1705.CrossRefGoogle Scholar
Gates, W. L. (1985), ‘The use of general circulation models in the analysis of the ecosystem impacts of climatic change’, Clim. Change 7, 267–284.CrossRefGoogle Scholar
GCOS (1998), ‘Report on the adequacy of the global climate observing system’, GCOS-48, Geneva.
Genest, C. and Favre, A.-C. (2007), ‘Everything you always wanted to know about copula modeling but were afraid to ask’, J. Hydrol. Eng. 12(4), 347–368.CrossRefGoogle Scholar
Georgakakos, A., Fleming, P., Dettinger, M., Peters-Lidard, C., Richmond, T. C., Reckhow, K., White, K. and Yates, D. (2014), Climate Change Impacts in the United States: The Third National Climate Assessment, U.S. Global Change Research Program, chapter ‘Water Resources’, pp. 69–112.
Gerrity, J. P. and McPherson, R. D. (1969), ‘Development of a limited area fine-mesh prediction model’, Mon. Wea. Rev. 97(9), 665–669.2.3.CO;2>CrossRefGoogle Scholar
Giannini, A., Saravanan, R. and Chang, P. (2003), ‘Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales’, Science 302(5647), 1027–1030.CrossRefGoogle ScholarPubMed
Gilks, W. R., Richardson, S. and Spiegelhalter, D. (1995), Markov chain Monte Carlo in practice, CRC Press.Google Scholar
Giorgi, F. (1990), ‘Simulation of regional climate using a limited-area model nested in a general circulation model’, J. Climate 3, 941–963.2.0.CO;2>CrossRefGoogle Scholar
Giorgi, F. and Bates, G. T. (1989), ‘The climatological skill of a regional climate model over complex terrain’, Mon. Wea. Rev. 117, 2325–2347.2.0.CO;2>CrossRefGoogle Scholar
Giorgi, F. and Gutowski, W. J. (2016), ‘Coordinated experiments for projections of regional climate change’, Curr. Clim. Change Rep. 2(4), 202–210.CrossRefGoogle Scholar
Giorgi, F., Hewitson, B., Christensen, J., Hulme, M., von Storch, H., Whetton, P., Jones, R., Mearns, L. and Fu, C. (2001), Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Regional Climate Information – Evaluation and Projections’.Google Scholar
Giorgi, F., Jones, C. and Asrar, G. R. (2009), ‘Addressing climate information needs at the regional level: the CORDEX framework’, WMO Bulletin 58(3), 175–183.Google Scholar
Giorgi, F., Marinucci, M. R. and Visconti, G. (1991), ‘A 2XCO2 climate change scenario over Europe generated using a limited area model nested in a general circulation model 2. Climate change scenario’, J. Geophys. Res. 97, 10011–10028.Google Scholar
Giorgi, F. and Mearns, L. O. (1999), ‘Introduction to special section: Regional Climate Modeling Revisited’, J. Geophys. Res. D6, 6335–6352.CrossRef
Giorgi, F., Torma, C., Coppola, E., Ban, N., Schär, C. and Somot, S. (2016), ‘Enhanced summer convective rainfall at Alpine high elevations in response to climate warming’, Nat. Geosci. 9(8), 584–589.CrossRefGoogle Scholar
Girshick, M. A. (1939), ‘On the sampling theory of roots of determinantal equations’, The Annals of Mathematical Statistics 10(3), 203–224.CrossRefGoogle Scholar
Girvetz, E. H., Maurer, E. P., Duffy, P. B., Ruesch, A., Thrasher, B. and Zganjar, C. (2013), Making climate data relevant to decision making: the important details of spatial and temporal downscaling, The World Bank.Google Scholar
Glahn, H. R. (1962), ‘An experiment in forecasting rainfall probabilities by objective methods’, Mon. Weather Rev 90, 59–67.2.0.CO;2>CrossRefGoogle Scholar
Glahn, H. R. and Allen, R. A. (1965), ‘A note concerning the “inflation” of regression forecasts’, J. Appl. Meteorol. 5, 124–126.Google Scholar
Glahn, H. R. and Lowry, D. A. (1972), ‘The use of model output statistics (MOS) in objective weather forecasing’, J. Appl. Meteorol. 11, 1203–1211.2.0.CO;2>CrossRefGoogle Scholar
Glasbey, C. A. and Nevison, I. M. (1997), Rainfall modelling using a latent Gaussian variable, in Modelling longitudinal and spatially correlated data, Springer, pp. 233–242.Google Scholar
Gleick, P. H. (1986), ‘Methods for evaluating the regional hydrologic impacts of global climatic changes’, J. Hydrol. 88(1), 97–116.CrossRefGoogle Scholar
Gneiting, T., Raftery, A. E., Westveld, A. H. and Goldman, T. (2005), ‘Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation’, Mon.Wea. Rev. 133, 1098–1118.CrossRefGoogle Scholar
Gobiet, A., Suklitsch, M. and Heinrich, G. (2015), ‘The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal’, Hydrol. Earth Syst. Sci. 19, 4055–4066.CrossRefGoogle Scholar
Goodess, C. M., Anagnostopoulou, C., Bárdossy, A., Frei, C., Harpham, C., Haylock, M. R., Hundecha, Y., Maheras, P., Ribalaygua, J., Schmidli, J., Schmith, T., Tolika, K., Tomozeiu, R. and Wilby, R. L. (2010), ‘An intercomparison of statistical downscaling methods for Europe and European regions – assessing their performance with respect to extreme weather events and the implications for climate change applications’, Project report, Climatic Research Unit, University of East Anglia, Norwich, UK.
Goosse, H. (2015), Climate system dynamics and modeling, Cambridge University Press.Google Scholar
Goosse, H. (2017), ‘Reconstructed and simulated temperature asymmetry between continents in both hemispheres over the last centuries’, Clim. Dynam. 48(5–6), 1483–1501.CrossRefGoogle Scholar
Goosse, H., Renssen, H., Timmermann, A., Bradley, R. S. and Mann, M. E. (2006), ‘Using paleoclimate proxy-data to select optimal realisations in an ensemble of simulations of the climate of the past millennium’, Clim. Dynam. 27(2–3), 165–184.CrossRefGoogle Scholar
Groisman, P. Y. and Legates, D. R. (1994), ‘The accuracy of United States precipitation data’, Bull. Amer. Meteorol. Soc. 75(2), 215–227.2.0.CO;2>CrossRefGoogle Scholar
Groot, A., Swart, R., Hygen, H., Benestad, R. E., Cauchy, A., Betgen, C. and Dubois, G. (2004), ‘ClipC deliverable user requirements, part 1: Strategies for user consultation and engagement and user requirements: Synthesis from past efforts’. www.clipc.eu/media/clipc/org/documents/ clipc_deliverable2_1_final_intemplate.pdf, accessed 2 August 2017.
Grotch, S. L. and MacCracken, M. C. (1991), ‘The use of general circulation models to predict regional climate change’, J. Climate 4, 286–303.2.0.CO;2>CrossRefGoogle Scholar
Güntner, A., Olsson, J., Calver, A. and Gannon, B. (2001), ‘Cascade-based disaggregation of continuous rainfall time series: the influence of climate’, Hydrol. Earth Syst. Sci. 5(2), 145– 164.CrossRefGoogle Scholar
Gutiérrez, J. M. et al. (2017), ‘An intercomparison of a large ensemble of statistical downscaling methods for Europe: overall results from the VALUE perfect predictor cross-validation experiment’, Int. J. Climatol., subm.
Gutiérrez, J. M., San-Martín, D., Brand s, S., Manzanas, R. and Herrera, S. (2013), ‘Reassessing statistical downscaling techniques for their robust application under climate change conditions’, J. Climate 26(1), 171–188.CrossRefGoogle Scholar
Gutmann, E., Pruitt, T., Clark, M. P., Brekke, L., Arnold, J. R., Raff, D. A. and Rasmussen, R. M. (2014), ‘An intercomparison of statistical downscaling methods used for water resource assessments in the United States’, Wat. Resour. Res. 50(9), 7167–7186.CrossRefGoogle Scholar
Gutowski, W. J., Decker, S. G., Donavon, R. A., Pan, Z., Arritt, R. W. and Takle, E. S. (2003), ‘Temporal-spatial scales of observed and simulated precipitation in central U.S. climate’, J. Climate 16, 3841–3847.2.0.CO;2>CrossRefGoogle Scholar
Haas, R. and Pinto, J. G. (2012), ‘A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms’, Geophys. Res. Lett. 39(23).CrossRefGoogle Scholar
Haas, R., Pinto, J. G. and Born, K. (2014), ‘Can dynamically downscaled windstorm footprints be improved by observations through a probabilistic approach?’, J. Geophys. Res. 119(2), 713– 725.Google Scholar
Haerter, J. O., Eggert, B., Moseley, C., Piani, C. and Berg, P. (2015), ‘Statistical precipitation bias correction of gridded model data using point measurements’, Geophys. Res. Lett. 42, 1919– 1929.CrossRefGoogle Scholar
Haerter, J. O., Hagemann, S., Moseley, C. and Piani, C. (2011), ‘Climate model bias correction and the role of timescales’, Hydrol. Earth Syst. Sci. 15(3), 1065–1079.CrossRefGoogle Scholar
Hagemann, S., Chen, C., Clark, D., Folwell, S., Gosling, S. N., Haddeland, I., Hannasaki, N., Heinke, J., Ludwig, F., Voss, F. and Wiltshire, A. (2013), ‘Climate change impact on available water resources obtained using multiple global climate and hydrology models’, Earth Syst. Dynam. 4, 129–144.CrossRefGoogle Scholar
Hagemann, S., Chen, C., Haerter, J. O., Heinke, J., Gerten, D. and Piani, C. (2011), ‘Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models’, J. Hydrometeorol. 12(4), 556–578.CrossRefGoogle Scholar
Haiden, T., Kann, A., Wittmann, C., Pistotnik, G., Bica, B. and Gruber, C. (2011), ‘The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the Eastern Alpine region’, Wea. Forecast. 26(2), 166–183.CrossRefGoogle Scholar
Haines, K. and Hannachi, A. (1995), ‘Weather regimes in the Pacific from a GCM’, J. Atmos. Sci. 52(13), 2444–2462.2.0.CO;2>CrossRefGoogle Scholar
Hall, A. (2014), ‘Projecting regional change’, Science 346(6216), 1461–1462.CrossRefGoogle ScholarPubMed
Hall, A., Qu, X. and Neelin, J. D. (2008), ‘Improving predictions of summer climate change in the United States’, Geophys. Res. Lett. 35, L01702.CrossRefGoogle Scholar
Hall, T., Brooks, H. E., Doswell, I. I.I. and Charles, A. (1999), ‘Precipitation forecasting using a neural network’, Wea. Forecast 14(3), 338–345.2.0.CO;2>CrossRefGoogle Scholar
Hannachi, A. (1997), ‘Low-frequency variability in a GCM: Three-dimensional flow regimes and their dynamics’, J. Climate 10(6), 1357–1379.2.0.CO;2>CrossRefGoogle Scholar
Hannachi, A., Jolliffe, I. T. and Stephenson, D. B. (2007), ‘Empirical orthogonal functions and related techniques in atmospheric science: A review’, Int. J. Climatol. 27(9), 1119–1152.CrossRefGoogle Scholar
Hanssen-Bauer, I., Achberger, C., Benestad, R. E., Chen, D. and Forland, E. J. (2005), ‘Statistical downscaling of climate scenarios over Scand inavia’, Clim. Res. 29(3), 255–268.CrossRefGoogle Scholar
Harris, I., Jones, P. D., Osborn, T. J. and Lister, D. H. (2014), ‘Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset’, Int. J. Climatol. 34(3), 623–642.CrossRefGoogle Scholar
Harvey, B. J., Shaffrey, L. C. and Woollings, T. J. (2015), ‘Deconstructing the climate change response of the Northern Hemisphere wintertime storm tracks’, Clim. Dynam. 45(9-10), 2847– 2860.CrossRefGoogle Scholar
Hastie, T. J. and Tibshirani, R. J. (1990), Generalized additive models, Chapman & Hall.Google ScholarPubMed
Hawkins, E., Smith, R. S., Gregory, J. M. and Stainforth, D. A. (2016), ‘Irreducible uncertainty in near-term climate projections’, Clim. Dynam. 46(11–12), 3807–3819.CrossRefGoogle Scholar
Hawkins, E. and Sutton, R. (2009), ‘The potential to narrow uncertainty in regional climate predictions’, Bull. Amer. Meteorol. Soc. 90(8), 1095–1107.CrossRefGoogle Scholar
Hawkins, E. and Sutton, R. (2011), ‘The potential to narrow uncertainty in projections of regional precipitation change’, Clim. Dynam. DOI:10.1007/s00382-010-0810-6.CrossRef
Hawkins, E. and Sutton, R. (2012), ‘Time of emergence of climate signals’, Geophys. Res. Lett. 39, L01702.CrossRefGoogle Scholar
Hay, L. E. and Clark, M. P. (2003), ‘Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States’, J. Hydrol. 282, 56–75.CrossRefGoogle Scholar
Hay, L. E., Clark, M. P., Wilby, R. L., Gutowski, W. J., Leavesley, G. H., Pan, Z., Arritt, R. W. and Takle, E. S. (2002), ‘Use of regional climate model output for hydrologic simulations’, J. Hydrometeorol. 3(5), 571–590.2.0.CO;2>CrossRefGoogle Scholar
Hay, L. E., McCabe, G. J., Wolock, D. M. and Ayers, M. A. (1991), ‘Simulation of precipitation by weather type analysis’, Wat. Resour. Res. 27, 493–501.CrossRefGoogle Scholar
Haylock, M. R., Gawley, G. C., Harpham, C., Wilby, R.L. and Goodess, C. M. (2006), ‘Downscaling heavy precipitation over the United Kingdom: A comparison of dynamical and statistical methods and their future scenarios’, Int. J. Climatol. 26(10), 1397–1415.CrossRefGoogle Scholar
Haylock, M. R., Hofstra, N., Klein Tank, A.M.G., Klok, E. J., Jones, P. D. and New, M. (2008), ‘A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006’, J. Geophys. Res. 113, 20119.CrossRefGoogle Scholar
Hazeleger, W., Severijns, C., Semmler, T., Stefanescu, S., Yang, S., Wang, X., Wyser, K., Dutra, E., Baldasano, J. M., Bintanja, R., Bougeault, P., Caballero, R., Ekman, A. M.L., Christensen, J. H., van den Hurk, B., Jimenez, P., Jones, C., Kållberg, P., Koenigk, T., Mc Grath, R., Mirand a, P., van Noije, T., Palmer, T., Parodi, J. A., Schmith, T., Selten, F., Storelvmo, T., Sterl, A., Tapamo, H., Vancoppenolle, M., Viterbo, P. and Willen, U. (2010), ‘EC-Earth: a seamless earthsystem prediction approach in action’, Bull. Amer. Meteorol. Soc. 91(10), 1357–1363.CrossRefGoogle Scholar
Hazeleger, W., van den Hurk, B.J. J., Min, E., van Oldenborgh, G. J., Petersen, A. C., Stainforth, D. A., Vasileiadou, E. and Smith, L. A. (2015), ‘Tales of future weather’, Nat. Clim. Change 5, 107–113.Google Scholar
He, J. and Soden, B. J. (2015), ‘Anthropogenic weakening of the tropical circulation: The relative roles of direct CO2 forcing and sea surface temperature change’, J. Climate 28(22), 8728– 8742.CrossRefGoogle Scholar
Heimann, D. (1986), ‘Estimation of regional surface layer wind field characteristics using a threelayer mesoscale model’, Beiträge zur Physik der Atmosphäre 59, 518–537.Google Scholar
Held, I. M. and Soden, B. J. (2006), ‘Robust responses of the hydrological cycle to global warming’, J. Climate 19(21), 5686–5699.CrossRefGoogle Scholar
Held, I. M., Ting, M. and Wang, H. (2002), ‘Northern winter stationary waves: theory and modeling’, J. Climate 15(16), 2125–2144.2.0.CO;2>CrossRefGoogle Scholar
Hempel, S., Frieler, K., Warszawski, L., Schewe, J. and Piontek, F. (2013), ‘A trend-preserving bias correction - the ISI-MIP approach’, Earth Syst. Dynam. 4, 219–236.CrossRefGoogle Scholar
Henderson-Sellers, A. (1996), ‘Can we integrate climatic modelling and assessment?’, Environ. Mod. Assess. 1(1–2), 59–70.Google Scholar
Hertig, E., Beck, C., Wanner, H. and Jacobeit, J. (2015), ‘A review of non-stationarities in climate variability of the last century with focus on the North Atlantic–European sector’, Earth Sci. Rev. 147, 1–17.CrossRefGoogle Scholar
Hertig, E. et al. (2017), ‘Validation of extremes from the Perfect-Predictor Experiment of the COST Action VALUE’, Int. J. Climatol., subm.
Hess, P. and Brezowsky, H. (1977), Katalog der Großwetterlagen Europas (1861–1976), Selbstverlag des Deutschen Wetterdienstes Bd. 15, Berichte des Deutschen Wetterdienstes, Offenbach am Main.Google Scholar
Hewitson, B. C. (2011), ‘Meeting user needs: climate service limits, ideals, & realities’, http://www.wcrp-climate.org/conference2011/orals/A6/Hewitson_A6.pdf.
Hewitson, B. C. (2016), ‘CORDEX gaps and the distillation dilemma’, http://www.icrccordex2016. org/images/pdf/Programme/presentations/plenary_1/Pl1_4_Hewitson_Bruce.pdf.
Hewitson, B. C. and Crane, R. G. (1994), Neural nets: applications in Geography, Springer.CrossRefGoogle Scholar
Hewitson, B. C. and Crane, R. G. (1996), ‘Climate downscaling: techniques and application’, Clim. Res. 7, 85–95.CrossRefGoogle Scholar
Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F. and Jack, C. (2014), ‘Interrogating empirical-statistical downscaling’, Clim. Change 122, 539–554.CrossRefGoogle Scholar
Hewitt, C. D. (2005), ‘The ENSEMBLES Project: Providing ensemble-based predictions of climate changes and their impacts’, EGGS newsletter 13, 22–25.Google Scholar
Hewitt, C., Mason, S. and Walland, D. (2012), ‘The global framework for climate services’, Nat. Clim. Change 2(12), 831–832.CrossRefGoogle Scholar
Hidalgo, H. G., Dettinger, M. D. and Cayan, D. R. (2008), ‘Downscaling with constructed analogues: Daily precipitation and temperature fields over the United States’. California Energy Commission, PIER Energy-Related Environmental Research. CEC-500-2007-123.
Hiebl, J. and Frei, C. (2016), ‘Daily temperature grids for Austria since 1961 – concept, creation and applicability’, Theor. Appl. Climatol. 124(1–2), 161–178.CrossRefGoogle Scholar
Hobaek Haff, I., Frigessi, A. and Maraun, D. (2015), ‘How well do regional climate models simulate the spatial structure of precipitation? An application of pair-copula constructions’, J. Geophys. Res. 120(7), 2624–2646.Google Scholar
Hofstätter, M., Ganekind, M. and Hiebl, J. (2013), ‘GPARD-6: A new 60-year gridded precipitation dataset for Austria based on daily rain gauge measurements’. Conference contribution: DACH 2013, Deutsh-Osterreichisch-Schweizerische Meteorologen-Tagung. Innsbruck, Austria.
Hofstra, N., New, M. and McSweeney, C. (2010), ‘The influence of interpolation and station network density on the distributions and trends of climate variables in daily gridded data’, Clim. Dynam. 35, 841–858.CrossRefGoogle Scholar
Holden, P. B. and Edwards, N. R. (2010), ‘Dimensionally reduced emulation of an AOGCM for application to integrated assessment modelling’, Geophys. Res. Lett. 37(21).CrossRefGoogle Scholar
Holton, J. R. and Hakim, G. J. (2013), An introduction to dynamic meteorology, 5 edn, Elsevier.
Hoppe, R., Wesselink, A. and Cairns, R. (2013), ‘Lost in the problem: the role of boundary organisations in the governance of climate change’, WIREs Clim. Change 4(4), 283–300.CrossRefGoogle Scholar
Hoskins, B. J. and Karoly, D. J. (1981), ‘The steady linear response of a spherical atmosphere to thermal and orographic forcing’, J. Atmos. Sci. 38(6), 1179–1196.2.0.CO;2>CrossRefGoogle Scholar
Hoskins, B. J. and Valdes, P. J. (1990), ‘On the existence of storm-tracks’, J. Atmos. Sci. 47(15), 1854–1864.2.0.CO;2>CrossRefGoogle Scholar
Hotelling, H. (1933), ‘Analysis of a complex of statistical variables into principal components’, J. Educ. Psychol. 24(6), 417.CrossRefGoogle Scholar
Hotelling, H. (1935), ‘The most predictable criterion’, J. Educ. Psychol. 26(2), 139.CrossRefGoogle Scholar
Howcroft, J. G. (1966), ‘Fine-mesh limited-area forecasting model’, Technical report U. S. Air Weather Service, Scott Air Force Base.
Hu, Y., Maskey, S. and Uhlenbrook, S. (2013), ‘Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods’, Theor. Appl. Climatol. 112(3–4), 447–460.CrossRefGoogle Scholar
Hughes, J. P. and Guttorp, P. (1994), ‘A class of stochastic models for relating synoptic atmospheric patterns to regional hydrologic phenomena’, Wat. Resour. Res. 30(5), 1535–1546.CrossRefGoogle Scholar
Hughes, J. P., Guttorp, P. and Charles, S. P. (1999), ‘A non-homogeneous hidden Markov model for precipitation occurrence’, J. Roy. Stat. Soc. C 48(1), 15–30.Google Scholar
Hulme, M. (2007), ‘The appliance of science’, The Guardian. www.theguardian.com/society/ 2007/mar/14/scienceofclimatechange.climatechange.
Hulme, M., Jenkins, G. J., Lu, X., Turnpenny, J. R., Mitchell, T.D., Jones, R. G., Lowe, J., Murphy, J.M., Hassell, D., Boorman, P., McDonald, R. and Hill, S. (2002), ‘Climate Change Scenarios for the United Kingdom. The UKCIP02 Scientific Report’, Technical report, Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK.Google Scholar
Huntingford, C. and Cox, P. M. (2000), ‘An analogue model to derive additional climate change scenarios from existing GCM simulations’, Clim. Dynam. 16(8), 575–586.CrossRefGoogle Scholar
Huth, R. (1999), ‘Statistical downscaling in central Europe: evaluation of methods and potential predictors’, Clim. Res. 13, 91–101, doi: 10.3354/cr013091.CrossRefGoogle Scholar
Huth, R. (2002), ‘Statistical downscaling of daily temperature in Central Europe’, J. Climate 15, 1731–1742.2.0.CO;2>CrossRefGoogle Scholar
Huth, R. (2004), ‘Sensitivity of local daily temperature change estimates to the selection of downscaling models and predictors’, J. Climate 17(3), 640–652.2.0.CO;2>CrossRefGoogle Scholar
Huth, R. (2005), ‘Downscaling of humidity variables: a search for suitable predictors and predictand s’, Int. J. Climatol. 25(2), 243–250.CrossRefGoogle Scholar
Huth, R., Kliegrova, S. and Metelka, L. (2008), ‘Non-linearity in statistical downscaling: does it bring an improvement for daily temperature in Europe?’, Int. J. Climatol. 28(4), 465–477.CrossRefGoogle Scholar
Huth, R., Miksovsky, J., Stepanek, P., Belda, M., Farda, A., Chladova, Z. and Pisoft, P. (2015), ‘Comparative validation of statistical and dynamical downscaling models on a dense grid in central Europe: temperature’, Theor. Appl. Climatol. 120(3–4), 533–553.CrossRefGoogle Scholar
Hyndman, R. J. and Grunwald, G. K. (2000), ‘Applications: Generalized additive modelling of mixed distribution Markov models with application to Melbourne's rainfall’, Aust. N. Z. J. Stat. 42(2), 145–158.CrossRefGoogle Scholar
Ineson, S. and Scaife, A. A. (2009), ‘The role of the stratosphere in the European climate response to El Niño’, Nat. Geosci. 2(1), 32–36.CrossRefGoogle Scholar
IPCC (1988), ‘Report of the first session of the WMO/UNEP Intergovernmental Panel on Climate Change (IPCC)’, World Climate Programme Publications Series. Geneva, 9–11 November.
Isotta, F. A., Frei, C., Weilguni, V., Lassegues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G., Ratto, S. M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Panettieri, C. R.E., Marigo, G. and Vertacnik, G. (2014), ‘The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine raingauge data’, Int. J. Climatol. 34(5), 1657–1675.CrossRefGoogle Scholar
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radtke, K., Rechid, D., Rounsevell, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R. and Weber, B. (2014), ‘EURO-CORDEX: New high-resolution climate change projections for European impact research’, Reg. Environ. Change 14, 563–578.Google Scholar
Jacobeit, J., Hertig, E., Seubert, S. and Lutz, K. (2014), ‘Statistical downscaling for climate change projections in the Mediterranean region: methods and results’, Reg. Environ. Change 14, 1891–1906.CrossRefGoogle Scholar
Jacobeit, J., Wanner, H., Luterbacher, J., Beck, C., Philipp, A. and Sturm, K. (2003), ‘Atmospheric circulation variability in the North-Atlantic-European area since the mid-seventeenth century’, Clim. Dynam. 20(4), 341–352.CrossRefGoogle Scholar
Jakob, C. (2014), ‘Going back to basics’, Nat. Clim. Change 4(12), 1042–1045.CrossRefGoogle Scholar
Joe, H. (1996), Distributions with fixed marginals and related topics, IMS, Hayward, CA, chapter ‘Families of m-variate distributions with given Margins and m(m-1)/2 Dependence Parameters’.Google Scholar
Jolliffe, I. T. (1986), Principal component analysis, Springer.CrossRefGoogle ScholarPubMed
Jolliffe, I. T. (1990), ‘Principal component analysis: a beginner's guide I. Introduction and application’, Weather 45(10), 375–382.CrossRefGoogle Scholar
Jolliffe, I. T. (1993), ‘Principal component analysis: A beginner's guide II. Pitfalls, myths and extensions’, Weather 48(8), 246–253.CrossRefGoogle Scholar
Jolliffe, I. T. (1995), ‘Rotation of principal components: choice of normalization constraints’, J. Appl. Stat. 22(1), 29–35.CrossRefGoogle Scholar
Jolliffe, I. T. and Stephenson, D. B., eds. (2003), Forecast verication: a practitioner's guide in atmospheric science, Wiley.Google Scholar
Jones, D. A., Wang, W. and Fawcett, R. (2009 a), ‘High-quality spatial climate data-sets for Australia’, Austral. Meteorol. Oceanogr. J. 58(4), 233.CrossRefGoogle Scholar
Jones, P. D., Harpham, C. and Briffa, K. R. (2013), ‘Lamb weather types derived from reanalysis products’, Int. J. Climatol. 33(5), 1129–1139.CrossRefGoogle Scholar
Jones, P. D., Kilsby, C. G., Harpham, C., Glenis, V. and Burton, A. (2009 b), ‘UK Climate Projections science report: Projections of future daily climate for the UK from the Weather Generator’, Technical report, University of Newcastle, UK.Google Scholar
Jones, P. D., Lister, D. H., Osborn, T. J., Harpham, C., Salmon, M. and Morice, C. P. (2012), ‘Hemispheric and large-scale land -surface air temperature variations: An extensive revision and an update to 2010’, J. Geophys. Res. 117(D5).CrossRefGoogle Scholar
Jones, P. D. and Moberg, A. (2003), ‘Hemispheric and large-scale surface air temperature variations: An extensive revision and an update to 2001’, J. Climate 16(2), 206–223.2.0.CO;2>CrossRefGoogle Scholar
Jordan, M. C. (1874), ‘Mémoires sur les formes bilinéaires’, J. de Mathémathiques Pures et Appliquées 19, 35–54.Google Scholar
Joyce, R. J., Janowiak, J. E., Arkin, P. A. and Xie, P. (2004), ‘CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution’, J. Hydrometeorol. 5(3), 487–503.2.0.CO;2>CrossRefGoogle Scholar
Jung, T., Miller, M. J., Palmer, T. N., Towers, P., Wedi, N., Achuthavarier, D., Adams, J. M., Altshuler, E. L., Cash, B. A., Kinter III, J. L., Marx, L., Stan, C. and Hodges, K. I. (2012), ‘High-resolution global climate simulations with the ECMWF model in Project Athena: Experimental design, model climate, and seasonal forecast skill’, J. Climate 25(9), 3155– 3172.CrossRefGoogle Scholar
Jury, M. W., Prein, A. F., Truhetz, H. and Gobiet, A. (2015), ‘Evaluation of CMIP5 models in the context of dynamical downscaling over Europe’, J. Climate 28(14), 5575–5582.CrossRefGoogle Scholar
Kaczmarska, J., Isham, V. and Onof, C. (2014), ‘Point process models for fine-resolution rainfall’, Hydrol. Sci. J. 59(11), 1972–1991.CrossRefGoogle Scholar
Kallache, M., Vrac, M. and Michelangeli, P.-A. (2011), ‘Nonstationary probabilistic downscaling of extreme precipitation’, J. Geophys. Res. 116, D05113.CrossRefGoogle Scholar
Kalnay, E. (2003), Atmospheric modeling, data assimilation and predictability, Cambridge University Press.Google Scholar
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gand in, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R. and Joseph, D. (1996), ‘The NCEP/NCAR reanalysis project’, Bull. Amer. Meteorol. Soc. 77, 437–471.2.0.CO;2>CrossRefGoogle Scholar
Karl, T. R., Diamond, H. J., Bojinski, S., Butler, J. H., Dolman, H., Haeberli, W., Harrison, D. E., Nyong, A., Rösner, S., Seiz, G., Trenberth, K., Westermeyer, W. and Zillman, J. (2010), ‘Observation needs for climate information, prediction and application: Capabilities of existing and future observing systems’, Proc. Environ. Sci. 1, 192–205.CrossRefGoogle Scholar
Karl, T. R., Wang, W.-C., Schlesinger, M. E., Knight, R. W. and Portman, D. (1990), ‘A method of relating general circulation model simulated climate to the observed local climate. Part I: seasonal statistics’, J. Climate 3, 1053–1079.2.0.CO;2>CrossRefGoogle Scholar
Katz, R. W. (1977), ‘Precipitation as a chain-dependent process’, J. Appl. Meteorol. 16(7), 671– 676.2.0.CO;2>CrossRefGoogle Scholar
Katz, R. W. and Parlange, M.B. (1993), ‘Effects of an index of atmospheric circulation on stochastic properties of precipitation’, Wat. Resour. Res. 29(7), 2335–2344.CrossRefGoogle Scholar
Katz, R. W. and Parlange, M. B. (1998), ‘Overdispersion phenomenon in stochastic modeling of precipitation’, J. Climate 11(4), 591–601.2.0.CO;2>CrossRefGoogle Scholar
Kay, A. L. and Jones, R. G. (2012), ‘Comparison of the use of alternative UKCP09 products for modelling the impacts of climate change on flood frequency’, Clim. Change 114(2), 211– 230.CrossRefGoogle Scholar
Keeley, S. P. E., Sutton, R. T. and Shaffrey, L. C. (2012), ‘The impact of North Atlantic sea surface temperature errors on the simulation of North Atlantic European region climate’, Q. J. R. Meteorol. Soc. 138, 1774–1783.CrossRefGoogle Scholar
Keeling, C. D. (1960), ‘The concentration and isotopic abundances of carbon dioxide in the atmosphere’, Tellus 12(2), 200–2003.CrossRefGoogle Scholar
Keenlyside, N. S., Latif, M., Jungclaus, J., Kornblueh, L. and Roeckner, E. (2008), ‘Advancing decadal-scale climate prediction in the North Atlantic sector’, Nature 453(7191), 84–88.CrossRefGoogle ScholarPubMed
Keller, D. E., Fischer, A. M., Liniger, M. A., Appenzeller, C. and Knutti, R. (2016), ‘Testing a weather generator for downscaling climate change projections over Switzerland ’, Int. J. Climatol.
Keller, D., Fischer, A. M., Frei, C., Liniger, M. A., Appenzeller, C. and Knutti, R. (2015), ‘Implementation and validation of a Wilks-type multi-site daily precipitation generator over a typical Alpine river catchment’, Hydrol. Earth Syst. Sci. 19, 2163–2177.CrossRefGoogle Scholar
Kendon, E. J., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C. and Senior, C. A. (2014), ‘Heavier summer downpours with climate change revealed by weather forecast resolution model’, Nat. Clim. Change 4, 570–576.CrossRefGoogle Scholar
Kendon, E. J., Rowell, D. P. and Jones, R. G. (2010), ‘Mechanisms and reliability of future projected changes in daily precipitation’, Clim. Dynam. 35(2–3), 489–509.CrossRefGoogle Scholar
Kennedy, M. C. and O'Hagan, A. (2001), ‘Bayesian calibration of computer models’, J. Roy. Stat. Soc. B 63(3), 425–464.CrossRefGoogle Scholar
Kerr, R. A. (2011a), ‘Time to adapt to a warming world, but where's the science?’, Science 334(6059), 1052–1053.CrossRefGoogle Scholar
Kerr, R. A. (2011b), ‘Vital details of global warming are eluding forecasters’, Science 334(6053), 173–174.CrossRefGoogle ScholarPubMed
Kettle, H. and Thompson, R. (2004), ‘Statistical downscaling in European mountains: verification of reconstructed air temperature’, Clim. Res. 26(2), 97–112.CrossRefGoogle Scholar
Kida, H., Koide, T., Sasaki, H. and Chiba, M. (1991), ‘A new approach for coupling a limited area model to a GCM for regional climate simulations’, J. Meteorol. Soc. Jap. 69(6), 723–728.CrossRefGoogle Scholar
Kilsby, C. G., Jones, P. D., Burton, A., Ford, A. C., Fowler, H. J., Harpham, C., James, P., Smith, A. and Wilby, R. L. (2007), ‘A daily weather generator for use in climate change studies’, Env. Mod. Soft. 22, 1705–1719.Google Scholar
Kim, J.-W., J.-T., Chang, Baker, N. L., Wilks, D. S. and Gates, W. L. (1984), ‘The statistical problem of climate inversion: determination of the relationship between local and large-scale climate’, Mon. Wea. Rev. 112, 2069–2077.2.0.CO;2>CrossRefGoogle Scholar
Kim, J., Waliser, D. E., Mattmann, C. A., Goodale, C. E., Hart, A. F., Zimdars, P. A., Crichton, D. J., Nikulin, C. J.G., Hewitson, B., Jack, C., Lennard, C. and Favre, A. (2014), ‘Evaluation of the CORDEX-Africa multi-RCM hindcast: systematic model errors’, Clim. Dynam. 42(5– 6), 1189–1202.CrossRefGoogle Scholar
Kistler, R., Kalnay, E., Collins, W., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R. and Fiorino, M. (2001), ‘The NCEP–NCAR 50-year reanalysis: monthly means CD–ROM and documentation’, Bull. Amer. Meteorol. Soc. 82(2), 247–267.2.3.CO;2>CrossRefGoogle Scholar
Klein Tank, A.M.G., Wijngaard, J. B., Können, G.P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern-Hansen, C., Heino, R., Bessemoulin, P., Müller- Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A.F.V., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., López, J.A., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexand er, L. V. and Petrovic, P. (2002), ‘Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment’, Int. J. Climatol. 22(12), 1441–1453.CrossRefGoogle Scholar
Klein Tank, A.M.G., Zwiers, F. W. and Zhang, X. (2009), Guidelines on analysis of extremes in a changing climate in support of informed decisions for adaptation, Climate Data and Monitoring WCDMP-No. 72, World Meteorological Organisation.Google Scholar
Klein, W. H. (1948), ‘Winter precipitation from the 700-millibar circulation’, Bull. Amer. Meteorol. Soc. 9, 439–453.Google Scholar
Klein, W. H. and Glahn, H. R. (1974), ‘Forecasting local weather by means of model output statistics’, Bull. Amer. Meteorol. Soc. 55(10), 1217–1227.2.0.CO;2>CrossRefGoogle Scholar
Klein, W. H., Lewis, B. M. and Enger, I. (1959), ‘Objective prediction of five-day mean temperatures during winter’, J. Meteorol. 16, 672–682.2.0.CO;2>CrossRefGoogle Scholar
Knight, J. R., Folland, C. K. and Scaife, A. A. (2006), ‘Climate impacts of the Atlantic multidecadal oscillation’, Geophys. Res. Lett. 33(17).CrossRefGoogle Scholar
Kobayash, S., Yukinari, O., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K. and Takahashi, K. (2015), ‘The JRA-55 reanalysis: general specifications and basic characteristics’, J. Meteorol. Soc. Japan II 93(1), 5–48.Google Scholar
Koenker, R. (2005), Quantile regression, number 38, Cambridge University Press.CrossRefGoogle Scholar
Kohonen, T. (1998), ‘The self-organizing map’, Neurocomputing 21(1–3), 1–6.Google Scholar
Kotlarski, S., Keuler, K., Christensen, O. B., Colette, A., Déqué, M., Gobiet, A., Goergen, K., Jacob, D., Lüthi, D., van Meijgaard, E., Nikulin, G., Schär, C., Teichmann, C., Vautard, R., Warrach-Sagi, K. and Wulfmeyer, V. (2014), ‘Regional climate modelling on European scales: a joint stand ard evaluation of the EURO-CORDEX RCM ensemble’, Geosci. Model. Dev. 7, 1297–1333.CrossRefGoogle Scholar
Krueger, O., Schenk, F., Feser, F. and Weisse, R. (2013), ‘Inconsistencies between longterm trends in storminess derived from the 20CR reanalysis and observations’, J. Climate 26(3), 868–874.CrossRefGoogle Scholar
Kruizinga, S. and Murphy, A. H. (1983), ‘Use of an analogue procedure to formulate objective probabilistic temperature forecasts in the netherland s’, Mon. Wea. Rev. 111(11), 2244–2254.2.0.CO;2>CrossRefGoogle Scholar
Kukla, G., Gavin, J. and Karl, T. R. (1986), ‘Urban warming’, J. Clim. Appl. Meteorol. 25(9), 1265–1270.2.0.CO;2>CrossRefGoogle Scholar
Kundzewicz, Z. W. and Stakhiv, E. Z. (2010), ‘Are climate models “ready for prime time” in water resources management applications, or is more research needed?’, Hydrol. Sci. J. 55, 1085– 1089.CrossRefGoogle Scholar
Kuo, H.-L. (1965), ‘On formation and intensification of tropical cyclones through latent heat release by cumulus convection’, J. Atmos. Sci. 22(1), 40–63.2.0.CO;2>CrossRefGoogle Scholar
Kutzbach, J. E. (1967), ‘Empirical eigenvectors of sea-level pressure, surface temperature and precipitation complexes over North America’, J. Appl. Meteorol. 6(5), 791–802.2.0.CO;2>CrossRefGoogle Scholar
Kysely, J. and Plavcova, E. (2010), ‘A critical remark on the applicability of E-OBS European gridded temperature data set for validating control climate simulations’, J. Geophys. Res. 115, D23118.CrossRefGoogle Scholar
Lackmann, G. M. (2015), ‘Hurricane Sand y before 1900 and after 2100’, Bull. Amer. Meteorol. Soc. 96(4), 547–560.CrossRefGoogle Scholar
Lall, U. and Sharma, A. (1996), ‘A nearest neighbor bootstrap for resampling hydrologic time series’, Wat. Resour. Res. 32(3), 679–693.CrossRefGoogle Scholar
Lamb, H. H. (1972), British Isles weather types and a register of the daily sequence of circulation patterns 1861–1971, HMSO London No. 116, Meteorol. Off. Geophys. Mem.Google Scholar
Laprise, R. (2008), ‘Regional climate modelling’, J. Comp. Phys. 227(7), 3641–3666.CrossRefGoogle Scholar
Laprise, R. (2014), ‘Comment on the added value to global model projections of climate change by dynamical downscaling: a case study over the continental US using the GISS-ModelE2 and WRF models” by Racherla et al.’, J Geophys. Res. 119(7), 3877–3881.Google Scholar
Latif, M. and Park, W. (2012), The future of the world's climate, Elsevier, Amsterdam, The Netherland s, chapter ‘Climatic Variability on Decadal to Century Time-Scales’, pp. 167–195.
Leadbetter, M. R., Lindgren, G. and Rootzen, H. (1983), Extremes and related properties of rand om sequences and processes, Springer Series in Statistics, Springer.CrossRefGoogle Scholar
Leand er, R. and Buishand, T. A. (2007), ‘Resampling of regional climate model output for the simulation of extreme river flows’, J. Hydro. 332(3), 487–496.Google Scholar
Legates, D. R. (1991), ‘The effect of domain shape on principal components analyses’, Int. J. Climatol. 11(2), 135–146.Google Scholar
Leloup, J., Lengaigne, M. and Boulanger, J.-P. (2008), ‘Twentieth century ENSO characteristics in the IPCC database’, Clim. Dynam. 30(2–3), 277–291.CrossRefGoogle Scholar
Lempert, R. J., Popper, S. W. and Banks, S. C. (2003), Shaping the next one hundred years. New methods for quantitative, long-term policy analysis, RAND.
Lempert, R., Nakicenovic, N., Sarewitz, D. and Schlesinger, M. (2004), ‘Characterising climatechange uncertainties for decision-makers’, Clim. Change 65, 1–9.CrossRefGoogle Scholar
Lenderink, G., Buishand, A. and van Deursen, W. (2007), ‘Estimates of future discharges of the river Rhine using two scenario methodologies: direct versus delta approach’, Hydrol. Earth Syst. Sci. 11(3), 1145–1159.CrossRefGoogle Scholar
Leonard, M., Westra, S., Phatak, A., Lambert, M., van den Hurk, B., McInnes, K., Risbey, J., Schuster, S., Jacob, D. and Stafford-Smith, M. (2014), ‘A compound event framework for understand ing extreme impacts’, WIREs Clim. Change 4(1), 113–128.Google Scholar
Li, C., Sinha, E., Horton, D. E., Diffenbaugh, N. S. and Michalak, A. M. (2014), ‘Joint bias correction of temperature and precipitation in climate model simulations’, J. Geophys. Res. 119(23), 13153–13162.Google Scholar
Li, G. and Xie, S.-P. (2014), ‘Tropical biases in CMIP5 multimodel ensemble: the excessive equatorial Pacific cold tongue and double ITCZ problems’, J. Climate 27(4), 1765– 1780.CrossRefGoogle Scholar
Li, H., Sheffield, J. and Wood, E. F. (2010), ‘Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching’, J. Geophys. Res. 115, D10101.Google Scholar
Limpasuvan, V., Thompson, D. W.J. and Hartmann, D. L. (2004), ‘The life cycle of the Northern Hemisphere sudden stratospheric warmings’, J. Climate 17(13), 2584–2596.2.0.CO;2>CrossRefGoogle Scholar
Liu, P. et al. (2009), ‘An MJO simulated by the NICAM at 14- and 7-km resolutions’, Mon. Wea. Rev. 137, 3254–3268.CrossRefGoogle Scholar
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R. and Brekke, L. (2015), ‘A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013’, Scientific Data 2, 150042.CrossRefGoogle Scholar
Lorenz, E. N. (1956), ‘Empirical orthogonal functions and statistical weather prediction’, Technical report, Massachusetts Institute of Technology, Dept. of Meteorology.
Lorenz, E. N. (1969), ‘Atmospheric predictability as revealed by naturally occuring analogs’, J. Atmos. Sci. 26, 639–646.2.0.CO;2>CrossRefGoogle Scholar
Lorenz, P. and Jacob, D. (2005), ‘Influence of regional scale information on the global circulation: A two-way nesting climate simulation’, Geophys. Res. Lett. 32, L18706.CrossRefGoogle Scholar
Lorenz, P. and Jacob, D. (2010), ‘Validation of temperature trends in the ENSEMBLES regional climate model runs driven by ERA40’, Clim. Res. 44, 167–177.CrossRefGoogle Scholar
Lovejoy, S. and Schertzer, D. (2010), ‘Towards a new synthesis for atmospheric dynamics: spacetime cascades’, Atmos. Res. 96(1), 1–52.CrossRefGoogle Scholar
Lu, J., Vecchi, G. A. and Reichler, T. (2007), ‘Expansion of the Hadley cell under global warming’, Geophys. Res. Lett. 34, L06805.Google Scholar
Luca, A. D., de Elía, R. and Laprise, R. (2012), ‘Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations’, Clim. Dynam. 38(5– 6), 1229–1247.Google Scholar
Lynch, A. H. and Cassado, J. J. (2006), Applied atmospheric dynamics, Wiley.Google Scholar
MacQueen, J. (1967), ‘Some methods for classification and analysis of multivariate observations’, in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, Univ. of Calif. Press, pp. 281–297.Google Scholar
Manabe, S., Smagorinsky, J. and Strickler, R. F. (1965), ‘Simulated climatology of a general circulation model with a hydrologic cycle’, Mon. Wea. Rev. 93(12), 769–798.2.3.CO;2>CrossRefGoogle Scholar
Manabe, S. and Wetherald, R. T. (1967), ‘Thermal equilibrium of the atmosphere with a given distribution of relative humidity’, J. Atmos. Sci. 24(3), 241–259.2.0.CO;2>CrossRefGoogle Scholar
Manabe, S. and Wetherald, R. T. (1975), ‘The effects of doubling the CO2 concentration on the climate of a general circulation model’, J. Atmos. Sci. 32(1), 3–15.2.0.CO;2>CrossRefGoogle Scholar
Mantua, N. J. and Hare, S. R. (2002), ‘The Pacific decadal oscillation’, J. Ocean. 58(1), 35– 44.CrossRefGoogle Scholar
Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. and Francis, R. C. (1997), ‘A Pacific interdecadal climate oscillation with impacts on salmon production’, Bull. Amer. Meteorol. Soc. 78(6), 1069–1079.2.0.CO;2>CrossRefGoogle Scholar
Maraun, D. (2012), ‘Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums’, Geophys. Res. Lett. 39, L06706.CrossRefGoogle Scholar
Maraun, D. (2013a), ‘Bias correction, quantile mapping and downscaling: revisiting the inflation issue’, J. Climate 26, 2137–2143.Google Scholar
Maraun, D. (2013b), ‘When will trends in European mean and heavy daily precipitation emerge?’, Env. Res. Lett. 8, 014004.CrossRefGoogle Scholar
Maraun, D. (2014), ‘Reply to comment on “Bias correction, quantile mapping and downscaling: revisiting the inflation issue”’, J. Climate 27, 1821–1825.CrossRefGoogle Scholar
Maraun, D. (2016), ‘Bias correcting climate change simulations - a critical review’, Curr. Clim. Change Rep. 2(4), 211–220.CrossRefGoogle Scholar
Maraun, D., Huth, R., Gutiérrez, J. M., San Martín, D., Dubrovsky, M., Fischer, A., Hertig, E., Soares, P. M.M., Bartholy, J., Pongrácz, R., Widmann, M., Casado, M. J., Ramos, P. & Bedia, J. (2017a), ‘The VALUE perfect predictor experiment: evaluation of temporal variability’, Int. J. Climatol., DOI 10.1002/joc.5222, published online.
Maraun, D., Osborn, T. J. and Gillett, N. P. (2008), ‘United Kingdom daily precipitation intensity: improved early data, error estimates and an update from 2000 to 2006’, Int. J. Climatol. 28(6), 833–842. DOI 10. 1002/joc. 1672.CrossRefGoogle Scholar
Maraun, D., Osborn, T. J. and Rust, H. W. (2012), ‘The influence of synptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation’, Clim. Dynam. 39, 287–301.CrossRefGoogle Scholar
Maraun, D., Rust, H. W. and Osborn, T. J. (2009), ‘The annual cycle of heavy precipitation across the UK: a model based on extreme value statistics’, Int. J. Climatol. 29, 1731–1744. DOI 10.1002/joc.1811.CrossRefGoogle Scholar
Maraun, D., Rust, H. W. and Osborn, T. J. (2010a), ‘Synoptic airflow and UK daily precipitation extremes. Development and validation of a vector generalised model’, Extremes 13, 133–153.CrossRefGoogle Scholar
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutierrez, J. M., Hagemann, S., Richter, I., Soares, P. M.M., Hall, A. and Mearns, L. (2017b), ‘Towards processinformed bias correction of climate change simulations’, Nat. Clim. Change, online first, DOI 10.1038/nclimate3418.
Maraun, D., Wetterhall, F., Ireson, A. M., Chand ler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K.C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M. and Thiele-Eich, I. (2010b), ‘Precipitation downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user’, Rev. Geophys. 48, RG3003.CrossRefGoogle Scholar
Maraun, D. and Widmann, M. (2015), ‘The representation of location by a regional climate model in complex terrain’, Hydrol. Earth Syst. Sci. 19, 3449–3456.CrossRefGoogle Scholar
Maraun, D. and Widmann, M. (2017), ‘Cross-validation of bias corrected climate simulations is misleading’, Hydrol. Earth Syst. Sci. in prep.
Maraun, D., Widmann, M., Gutierrez, J. M., Kotlarski, S., Chand ler, R. E., Hertig, E., Wibig, J., Huth, R. and Wilcke, R. A.I. (2015), ‘VALUE: A framework to validate downscaling approaches for climate change studies’, Earth's Future 3, 1–14.CrossRefGoogle Scholar
Masato, G., Hoskins, B. J. and Woollings, T. (2013), ‘Winter and summer Northern Hemisphere blocking in CMIP5 models’, J. Climate 26, 7044–7059.CrossRefGoogle Scholar
Mason, S. J. (2004), ‘Simulating climate over western North America using stochastic weather generators’, Clim. Change 62(1), 155–187.CrossRefGoogle Scholar
Masson-Delmotte, V., Schulz, M., Abe-Ouchi, A., Beer, J., Ganopolski, A., González Rouco, J. F., Jansen, E., Lambeck, K., Luterbacher, J., Naish, T., Osborn, T., Otto-Bliesner, B., Quinn, T., Ramesh, R., Rojas, M., Shao, X. and Timmermann, A. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Information from Paleoclimate Archives’.Google Scholar
Mastrand rea, M. D., Heller, N. E., Root, T. L. and Schneider, S. H. (2010), ‘Bridging the gap: linking climate-impacts research with adaptation planning and management’, Clim. Change 100(1), 87–101.Google Scholar
Matsikaris, A., Widmann, M. and Jungclaus, J. (2016), ‘Assimilating continental mean temperatures to reconstruct the climate of the late pre-industrial period’, Clim. Dynam. 46(11– 12), 3547–3566.CrossRefGoogle Scholar
Matulla, C., Zhang, X., Wang, X. L., Wang, J., Zorita, E., Wagner, S. and Von Storch, H. (2008), ‘Influence of similarity measures on the performance of the analog method for downscaling daily precipitation’, Clim. Dynam. 30(2–3), 133–144.CrossRefGoogle Scholar
Matyasovszky, I., Bogardi, I., Bardossy, A. and Duckstein, L. (1993), ‘Space-time precipitation reflecting climate change’, Hydrol. Sci. J. 38(6), 539–558.CrossRefGoogle Scholar
Maurer, E. P. (2007), ‘Fine-resolution climate projections enhance regional climate change impact studies’, EOS 88(47), 504.CrossRefGoogle Scholar
Maurer, E. P., Brekke, L., Pruitt, T., Thrasher, B., Long, J., Duffy, P., Dettinger, M., Cayan, D. and Arnold, J. (2014), ‘An enhanced archive facilitating climate impact and adaptation analysis’, Bull. Amer. Meteorol. Soc. 95(7), 1011–1019.CrossRefGoogle Scholar
Maurer, E. P. and Hidalgo, H. G. (2008), ‘Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods’, Hydrol. Earth Syst. Sci. 12, 551– 563.CrossRefGoogle Scholar
Maurer, E. P., Hidalgo, H. G., Das, T., Dettinger, M. D. and Cayan, D. R. (2010), ‘The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California’, Hydrol. and Earth System Sci. 14(6), 1125–1138.CrossRefGoogle Scholar
Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P. and Nijssen, B. (2002), ‘A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States’, J. Climate 15(22), 3237–3251.2.0.CO;2>CrossRefGoogle Scholar
McBean, G., McCarthy, J., Browning, K., Morel, P. and Rasool, I. (1990), Climate Change. The IPCC Scientific Assessment. Report prepared for Intergovernmental Panel on Climate Change byWorking Group I, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Narrowing the Uncertainties: A Scientific Action Plan for Improved Prediction of Global Climate Change’.Google Scholar
McCabe, G. J., Palecki, M. A. and Betancourt, J. L. (2004), ‘Pacific and Atlantic Ocean influences on multidecadal drought frequency in the United States’, Proc. Nat. Acad. Sci. 101(12), 4136– 4141.CrossRefGoogle ScholarPubMed
McCullagh, P. and Nelder, J. A. (1983), Generalized linear Models, Chapman and Hall, London.CrossRefGoogle Scholar
McGuffie, K. and Henderson-Sellers, A. (2005), A climate modelling primer, JohnWiley & Sons.CrossRefGoogle Scholar
Mearns, L. O., Arritt, R., Biner, S., Bukovsky, M. S., McGinnis, S., Sain, S., Caya, D., Correia, J., Flory, D., Gutowski, W., Takle, E. S., Jones, R., Leung, R., Moufouma-Okia, W., McDaniel, L., Nunes, A. M.B., Qian, Y., Roads, J., Sloan, L. and Snyder, M. (2012), ‘The North American Regional Climate Change Assessment Program. Overview of Phase I Results’, Bull. Amer. Meteorol. Soc. 93, 1337–1362.CrossRefGoogle Scholar
Mearns, L. O., Gutowski, W. J., Jones, R., Leung, L.-Y., McGinnis, S., Nunes, A. M.B. and Qian, Y. (2009), ‘A regional climate change assessment program for North America’, EOS 90(36), 311–312.CrossRefGoogle Scholar
Mearns, L. O., Katz, R. W. and Schneider, S. H. (1984), ‘Extreme high-temperature events: changes in their probabilities with changes in mean temperature’, J. Clim. Appl. Meteorol. 23(12), 1601–1613.2.0.CO;2>CrossRefGoogle Scholar
Meehl, G. A., Covey, C., Delworth, T., Latif, M., McAvaney, B., Mitchell, J., Stouffer, R. and Taylor, K. (2007a), ‘The WCRP CMIP3 multi-model dataset: a new era in climate change research’, Bull. Amer. Meteorol. Soc. 88, 1383–1394.CrossRefGoogle Scholar
Meehl, G. A., Goddard, L., Murphy, J., Stouffer, R. J., Boer, G., Danabasoglu, G., Dixon, K., Giorgetta, M. A., Greene, A. M., Hawkins, E., Hegerl, G., Karoly, D., Keenlyside, N., Kimoto, M., Kirtman, B., Navarra, A., Pulwarty, R., Smith, D., Stammer, D. and Stockdale, T. (2009), ‘Decadal prediction. Can it be skillfull?’, Bull. Amer. Meteorol. Soc. 90, 1467–1485.CrossRefGoogle Scholar
Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C.B., Watterson, I. G., Weaver, A. J. and Zhao, Z.-C. (2007b), Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Global Climate Projections’.Google Scholar
Meredith, E. P., Maraun, D., Semenov, V. A. and Park, W. (2015a), ‘Evidence for added value of convection permitting models for studying changes in extreme precipitation’, J. Geophys. Res. Atmos. 120, 12, 500–12, 513.CrossRefGoogle Scholar
Meredith, E. P., Semenov, V. A., Maraun, D., Park, W. and Chernokulsky, A. V. (2015b), ‘Crucial role of Black Sea warming in amplifying the 2012 Krymsk precipitation extreme’, Nat. Geosci. 8(8), 615–619.CrossRefGoogle Scholar
Merlis, T. M. (2015), ‘Direct weakening of tropical circulations from masked CO2 radiative forcing’, Proc. Nat. Acad. Sci. 112(43), 13167–13171.CrossRefGoogle ScholarPubMed
Mestre, O., Gruber, C., Prieur, C., Caussinus, H. and Jourdain, S. (2011), ‘SPLIDHOM: A method for homogenization of daily temperature observations’, J. Appl. Meteorol. Climatol. 50(11), 2343–2358.CrossRefGoogle Scholar
Mezghani, A. and Hingray, B. (2009), ‘A combined downscaling-disaggregation weather generator for stochastic generation of multisite hourly weather variables over complex terrain: development and multi-scale validation for the Upper Rhone River basin’, J. Hydrol. 377(3), 245– 260.CrossRefGoogle Scholar
Michelangeli, P.-A., Vrac, M. and Loukos, H. (2009), ‘Probabilistic downscaling approaches: application to wind cumulative distribution functions’, Geophys. Res. Lett. 36(11).CrossRefGoogle Scholar
Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S.-P. and Small, R. J. (2008), ‘Influence of the Gulf Stream on the troposphere’, Nature 452(7184), 206–209.CrossRefGoogle ScholarPubMed
Mitchell, J. F. B., Johns, T. C., Eagles, M., Ingram, W. J. and Davis, R. A. (1999), ‘Towards the construction of climate change scenarios’, Clim. Change 41(3), 547–581.CrossRefGoogle Scholar
Moron, V., Robertson, A. W., Ward, M.N. and Ndiaye, O. (2008), ‘Weather types and rainfall over Senegal. Part I: Observational analysis’, J. Climate 21(2), 266–287.Google Scholar
Mountain Research Initiative EDW Working Group (2015), ‘Elevation-dependent warming in mountain regions of the world’, Nat. Clim. Change 5(5), 424–430.
Murphy, J. M., Sexton, D. M.H., Jenkins, G. J., Booth, B. B.B., Brown, C. C., Clark, R. T., Collins, M., Harris, G. R., Kendon, E. J., Betts, R. A., Brown, S. J., Humphrey, K. A., McCarthy, M. P., McDonald, R. E., Stephens, A., Wallace, C., Warren, R., Wilby, R. and Wood, R. A. (2009), ‘UK climate projections science report: Climate change projections’, Technical report, Met Office Hadley Centre, Exeter UK.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T. and Zhang, H. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Anthropogenic and Natural Radiative Forcing’.Google Scholar
Nakamura, H., Sampe, T., Goto, A., Ohfuchi, W. and Xie, S.-P. (2008), ‘On the importance of midlatitude oceanic frontal zones for the mean state and dominant variability in the tropospheric circulation’, Geophys. Res. Lett. 35, L15709.CrossRefGoogle Scholar
Nakicenovic, N. and Swart, R., eds (2000), Special report on emissions scenarios: a special report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press.Google Scholar
Nature (2010), ‘Validation required’, Nature 463(7283), 849–849.PubMed
Neelin, J. D. (2010), Climate change and climate modeling, Cambridge University Press.CrossRefGoogle Scholar
New, M., Hulme, M. and Jones, P. (1999), ‘Representing twentieth-century space–time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology’, J. Climate 12(3), 829–856.2.0.CO;2>CrossRefGoogle Scholar
New, M., Hulme, M. and Jones, P. (2000), ‘Representing twentieth-century space–time climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate’, J. Climate 13(13), 2217–2238.2.0.CO;2>CrossRefGoogle Scholar
North, G. R., Bell, T. L., Cahalan, R. F. and Moeng, F. J. (1982), ‘Sampling errors in the estimation of empirical orthogonal functions’, Mon. Wea. Rev. 110(7), 699–706.2.0.CO;2>CrossRefGoogle Scholar
Northrop, P. (1998), ‘A clustered spatial-temporal model of rainfall’, Proc. Roy. Soc. A 454(1975), 1875–1888.CrossRefGoogle Scholar
Novella, N. S. and Thiaw, W. M. (2013), ‘African rainfall climatology version 2 for famine early warning systems’, J. Appl. Meteorol. Climatol. 52(3), 588–606.CrossRefGoogle Scholar
Obukhov, A. M. (1947), ‘Statistically homogeneous fields on a sphere’, Usp. Mat. Nauk 2(2), 196– 198.Google Scholar
Obukhov, A. M. (1954), ‘Statistical description of continuous fields’, Transactions of the Geophysical International Academy Nauk USSR 24(24), 3–42.Google Scholar
Obukhov, A. M. (1960), ‘The statistically orthogonal expansion of empirical functions’, Bulletin of the Academy of Sciences of the USSR. Geophysics Series (English Transl.) 1, 288– 291.Google Scholar
O'Hagan, T. (2004), ‘Dicing with the unknown’, Significance 1(3), 132–133.CrossRefGoogle Scholar
O'Hare, G., Sweeney, J. and Wilby, R. (2014), Weather, climate and climate change: human perspectives, Routledge.
Olsson, J. (1998), ‘Evaluation of a scaling cascade model for temporal rain-fall disaggregation’, Hydrol. Earth Syst. Sci. 2(1), 19–30.CrossRefGoogle Scholar
Olsson, J., Uvo, C. and Jinno, K. (2001), ‘Statistical atmospheric downscaling of short-term extreme rainfall by neural networks’, Phys. Chem. Earth B 26(9), 695–700.CrossRefGoogle Scholar
Onof, C., Chand ler, R. E., Kakou, A., Northrop, P., Wheater, H.S. and Isham, V. (2000), ‘Rainfall modelling using Poisson-cluster processes: a review of developments’, Stoc. Env. Res. Risk Assess. 14(6), 384–411.CrossRefGoogle Scholar
Onof, C. and Wheater, H. S. (1994), ‘Improvements to the modelling of British rainfall using a modified rand om parameter Bartlett-Lewis rectangular pulse model’, J. Hydrol. 157(1), 177– 195.CrossRefGoogle Scholar
Osborn, T. J. and Hulme, M. (1997), ‘Development of a relationship between station and grid-box rainday frequencies for climate model evaluation’, J. Climate 10(8), 1885–1908.2.0.CO;2>CrossRefGoogle Scholar
Osborn, T. J., Wallace, C. J., Harris, I. C. and Melvin, T. M. (2016), ‘Pattern scaling using ClimGen: monthly-resolution future climate scenarios including changes in the variability of precipitation’, Clim. Change 134, 353–369.CrossRefGoogle Scholar
Over, T. M. and Gupta, V. K. (1996), ‘A space-time theory of mesoscale rainfall using rand om cascades’, J. Geophys. Res. 101(D21), 26319–26331.CrossRefGoogle Scholar
Palmer, T. N. (2013), ‘Climate extremes and the role of dynamics’, Proc. Nat. Acad. Sci. 110, 5281–5282.CrossRefGoogle ScholarPubMed
Panofsky, H. W. and Brier, G. W. (1968), Some applications of statistics to meteorology, The Pennsylvania State University Press.Google Scholar
Parker, D. E. (2010), ‘Urban heat island effects on estimates of observed climate change’, WIREs Clim. Change 1(1), 123–133.CrossRefGoogle Scholar
Paschalis, A., Molnar, P., Fatichi, S. and Burland o, B. (2013), ‘A stochastic model for highresolution space-time precipitation simulation’, Wat. Resour. Res. 49(12), 8400–8417.CrossRefGoogle Scholar
Paul, S., Liu, C. M., Chen, J. M. and Lin, S. H. (2008), ‘Development of a statistical downscaling model for projecting monthly rainfall over east asia from a general circulation model output’, J. Geophys. Res. 113(D15).CrossRefGoogle Scholar
Pearson, K. (1901), ‘On lines and planes of closest fit to systems of points in space’, Philosophical Magazine 2(11), 559–572.Google Scholar
Peixoto, J. P. and Oort, A. H. (1992), Physics of climate, American Institute of Physics.
Penalba, O. C., Rivera, J. A. and Pántano, V.C. (2014), ‘The CLARIS LPB database: constructing a long-term daily hydro-meteorological dataset for La Plata Basin, southern South America’, Geosci. Data J. 1(1), 20–29.CrossRefGoogle Scholar
Perica, S. and Foufoula-Georgiou, E. (1996), ‘Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions’, J. Geophys. Res 101, 26–347.CrossRefGoogle Scholar
Philand er, S. G. (1990), El Niño, La Niña, and the Southern Oscillation, Academic Press.
Philipp, A., Bartholy, J., Beck, C., Erpicum, M., Esteban, P., Fettweis, X., Huth, R., James, P., Jourdain, S., Kreienkamp, F. et al. (2010), ‘Cost733cat - A database of weather and circulation type classifications’, Phys. Chem. Earth 35(9), 360–373.CrossRefGoogle Scholar
Philipp, A., Beck, C., Huth, R. and Jacobeit, J. (2014), ‘Development and comparison of circulation type classifications using the COST 733 dataset and software’, Int. J. Climatol.
Philipp, A., Della-Marta, P.-M., Jacobeit, J., Fereday, D. R., Jones, P. D., Moberg, A. and Wanner, H. (2007), ‘Long-term variability of daily North Atlantic-European pressure patterns since 1850 classified by simulated annealing clustering’, J. Climate 20(16), 4065–4095.CrossRefGoogle Scholar
Piani, C. and Haerter, J. O. (2012), ‘Two dimensional bias correction of temperature and precipitation copulas in climate models’, Geophys. Res. Lett. 39(20), L20401.CrossRefGoogle Scholar
Piani, C., Haerter, J. O. and Coppola, E. (2010a), ‘Statistical bias correction for daily precipitation in regional climate models over Europe’, Theor. Appl. Climatol. 99(1–2), 187–192.CrossRefGoogle Scholar
Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S. and Haerter, J. O. (2010b), ‘Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models’, J. Hydrol. 395, 199–215.CrossRefGoogle Scholar
Pielke Jr., R. A. (2007), The honest broker: making sense of science in policy and politics, Cambridge University Press.CrossRefGoogle Scholar
Pielke, R. A. and Wilby, R. L. (2012), ‘Regional climate downscaling: What's the point?’, EOS 93(5), 52–53.CrossRefGoogle Scholar
Pielke, R., Beven, K., Brasseur, G., Calvert, J., Chahine, M., Dickerson, R. R., Entekhabi, D., Foufoula-Georgiou, E., Gupta, H., Gupta, V., Krajewski, W., Krieder, E. P., Lau, W. K.M., Mc Donnell, J., Rossow, W., Schaake, J., Smith, J., Sorooshian, S. and Wood., E. (2009), ‘Climate change: the need to consider human forcings besides greenhouse gases’, EOS 90(45), 413–413.CrossRefGoogle Scholar
Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J. T. and Hegewisch, K. C. (2015), ‘Improved bias correction techniques for hydrological simulations of climate change’, J. Hydrometeorol. 16(6), 2421–2442.CrossRefGoogle Scholar
Pierce, D. W., Cayan, D. R. and Thrasher, B. L. (2014), ‘Statistical downscaling using localized constructed analogs (LOCA)’, J. Hydrometeorol. 15(6), 2558–2585.CrossRefGoogle Scholar
Pingel, S., ed. (2012), Toward a climate services enterprise, Brussels, Belgium. Conference Report.
Pinto, J. G., Neuhaus, C. P., Leckebusch, G. C., Reyers, M. and Kerschgens, M. (2010), ‘Estimation of wind storm impacts over western germany under future climate conditions using a statistical–dynamical downscaling approach’, Tellus A 62(2), 188–201.CrossRefGoogle Scholar
Planton, S., ed. (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom, and New York, NY, USA, chapter ‘Annex III: Glossary’.Google Scholar
Plaut, G. and Simonnet, E. (2001), ‘Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe’, Clim. Res. 17(3), 303–324.CrossRefGoogle Scholar
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart, F., Laloyaux, P., Tan, D. G.H., Peubey, C., Thépaut, J.-N., Yannick, T., Hólm, E.V., Bonavita, M., Isaksen, L. and Fisher, M. (2016), ‘ERA-20C: An atmospheric reanalysis of the twentieth century’, J. Climate 29, 4083–4097.CrossRefGoogle Scholar
Prein, A. F., Gobiet, A., Suklitsch, M., Truhetz, H., Awan, N. K., Keuler, K. and Georgievski, G. (2013), ‘Added value of convection permitting seasonal simulations’, Clim. Dynam. 41, 2655– 2677.CrossRefGoogle Scholar
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N.P.M. and Leung, R. (2015), ‘A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges’, Rev. Geophys. 53(2), 323–361.CrossRefGoogle ScholarPubMed
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P. and Holland, G. J. (2017), ‘The future intensification of hourly precipitation extremes’, Nat. Clim. Change 7, 48–52.CrossRefGoogle Scholar
Preisendorfer, R. W. and Barnett, T. P. (1977), Significance tests for empirical orthogonal functions, in Fifth Conf. on Probability and Statistics in Atmos. Sci., Las Vegas, NV. American Meteorol. Soc, Vol. 169, p. 172.
Preisendorfer, R. W. and Mobley, C. D. (1988), Principal component analysis in meteorology and oceanography, Developments in atmospheric science, Elsevier.
Preisendorfer, R. W., Zwiers, F. W. and Barnett, T. P. (1981), ‘Foundations of principal component selection rules’, SIO Reference Series 81–4 May 1981.
prepdata (n.d.), ‘Partnership for Resilience and Preparedness (PREP)’, www.prepdata.org.
Prudhomme, C., Reynard, N. and Crooks, S. (2002), ‘Downscaling of global climate models for flood frequency analysis: where are we now?’, Hydrol. Proc. 16(6), 1137–1150. Sp. Iss. SI.CrossRefGoogle Scholar
Prudhomme, C., Wilby, R.L., Crooks, S., Kay, A. L. and Reynard, N. S. (2010), ‘Scenario-neutral approach to climate change impact studies: application to flood risk’, J. Hydrol. 390, 198–209.CrossRefGoogle Scholar
Pruppacher, H. R., Klett, J. D. and Wang, P. K. (1998), Microphysics of clouds and precipitation, Taylor & Francis.Google Scholar
Pryor, S. C., Schoof, J. T. and Barthelmie, R. J. (2005), ‘Empirical downscaling of wind speed probability distributions’, J. Geophys. Res. 110, D19109.CrossRefGoogle Scholar
Racherla, P. N., Shindell, D. T. and Faluvegi, G. S. (2012), ‘The added value to global model projections of climate change by dynamical downscaling: a case study over the continental US using the GISS-ModelE2 and WRF models’, J. Geophys. Res. 117(D20).CrossRefGoogle Scholar
Racsko, P., Szeidl, L. and Semenov, M. (1991), ‘A serial approach to local stochastic weather models’, Ecol. Mod. 57(1–2), 27–41.CrossRefGoogle Scholar
Radanovics, S., Vidal, J.-P., Sauquet, E., Daoud, A. B. and Bontron, G. (2013), ‘Optimising predictor domains for spatially coherent precipitation downscaling’, Hydrol. Earth Syst. Sci. 17, 4189–4208.CrossRefGoogle Scholar
Räisänen, J. and Räty, O. (2013), ‘Projections of daily mean temperature variability in the future: cross-validation tests with ENSEMBLES regional climate simulations’, Clim. Dynam. 41, 1553–1568.CrossRefGoogle Scholar
Rajczak, J., Kotlarski, S. and Schär, C. (2016), ‘Does quantile mapping of simulated precipitation correct for biases in transition probabilities and spell lengths?’, J. Climate 29, 1605–1615.CrossRefGoogle Scholar
Rand all, D. (2015), An introduction to the global circulation of the atmosphere, Princeton University Press.Google Scholar
Räty, O., Räisänen, J. and Ylhäisi, J.S. (2014), ‘Evaluation of delta change and bias correction methods for future daily precipitation: intermodel cross-validation using ENSEMBLES simulations’, Clim. Dynam. 42(9–10), 2287–2303.CrossRefGoogle Scholar
Raymond, D. J. and Emanuel, K. A. (1993), The Kuo cumulus parameterization, in The representation of cumulus convection in numerical models, Springer, pp. 145–147.
Rebora, N., Ferraris, L., von Hardenberg, J. and Provenzale, A. (2006), ‘RainFARM: rainfall downscaling by a filtered autoregressive model’, J. Hydrometeorol. 7(4), 724–738.CrossRefGoogle Scholar
Reyers, M., Pinto, J. G. and Moemken, J. (2015), ‘Statistical–dynamical downscaling for wind energy potentials: evaluation and applications to decadal hindcasts and climate change projections’, Int. J. Climatol. 35(2), 229–244.CrossRefGoogle Scholar
Richardson, C. W. (1981), ‘Stochastic simulation of daily precipitation, temperature, and solar radiation’, Wat. Resour. Res. 17(1).CrossRefGoogle Scholar
Richardson, C. W. and Wright, D. A. (1984), ‘WGEN: A model for generating daily weather variables’, Report No. 8, Agricultural Research Service, US Department of Agriculture, Washington, DC.
Richman, M. (1987), ‘Rotation of principal components: a reply’, J. Climatol. 7(5), 511–520.CrossRefGoogle Scholar
Richman, M. B. (1986), ‘Rotation of principal components’, J. Climatol. 6(3), 293–335.CrossRefGoogle Scholar
Richter, I. (2015), ‘Climate model biases in the eastern tropical oceans: causes, impacts and ways forward’, WIRES: Clim. Change 6(3), 345–358.Google Scholar
Richter, I., Chang, P., Xu, Z., Doi, T., Kataoka, T., Nagura, M., Oettli, P., de Szoeke, S. and Tozuka, T. (2016), Indo-Pacific climate variability and predictability, World Scientific, chapter ‘An Overview of Coupled GCM Performance in the Tropics’.
Richter, I. and S.-P. Xie (2008), ‘On the origin of equatorial Atlantic biases in coupled general circulation models’, Clim. Dynam. 31(5), 587–598.CrossRefGoogle Scholar
Richter, I., Xie, S.-P., Behera, S. K., Doi, T. and Masumoto, Y. (2014), ‘Equatorial Atlantic variability and its relation to mean state biases in CMIP5’, Clim. Dynam. 42(1–2), 171–188.CrossRefGoogle Scholar
Rienecker, M.M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom, S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J., Koster, R. D., Lucchesi, R., Molo, A., Owens, T., Pawson, S., Pegion, P., Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz, M. and Woollen, J. (2011), ‘MERRA: NASA's modern-era retrospective analysis for research and applications’, J. Climate 24(14), 3624–3648.CrossRefGoogle Scholar
Rockel, B. (2015), ‘The regional downscaling approach: a brief history and recent advances’, Curr. Clim. Change. Rep. 1(1), 22–29.CrossRefGoogle Scholar
Rodriguez-Iturbe, I., Cox, D. R. and Isham, V. (1987), ‘Some models for rainfall based on stochastic point processes’, in Proc. Roy. Soc. A, Vol. 410, pp. 269–288.CrossRefGoogle Scholar
Rodriguez-Iturbe, I., Cox, D. R. and Isham, V. (1988), ‘A point process model for rainfall: further developments’, in Proc. Roy. Soc. A, Vol. 417, The Royal Society, pp. 283–298.CrossRefGoogle Scholar
Roe, G. H. (2005), ‘Orographic precipitation’, Ann. Rev. Earth Planet. Sci. 33, 645–671.CrossRefGoogle Scholar
Roehrig, R., Bouniol, D., Guichard, F., Hourdin, H. and Redelsperger, J.-L. (2013), ‘The present and future of the West African monsoon: a process-oriented assessment of CMIP5 simulations along the AMMA transect’, J. Climate 26(17), 6471–6505.CrossRefGoogle Scholar
Roessler, O., Fischer, A. M., Huebener, H., Maraun, D., Benestad, R. E., Christodoulides, P., Soares, P. M.M., Cardoso, R. M., Pagé, C., Kanamaru, H., Kreienkamp, F. and Vlachogiannis, D. (2017), ‘Challenges to link climate change data provision and user needs - perspective from the COST-Action VALUE’, Int. J. Climatol. DOI: 10.1002/joc.5060.CrossRef
Rogers, R. R. and Yau, M. K. (1996), A short course in cloud physics, Elsevier.Google Scholar
Rosenzweig, C. (1985), ‘Potential CO2-induced climate effects on North American wheatproducing regions’, Clim. Change 7, 367–389.CrossRefGoogle Scholar
Rummukainen, M. (1997), ‘Methods of statistical downscaling of GCM simulations. Reports Meteorology and Climatology 80’, Technical report, SwedishMeteorological and Hydrological Institute, SE-601 76 Norrköping, Sweden.
Rummukainen, M. (2010), ‘State-of-the-art with regional climate models’, WIREs Clim. Change 1, 82–96. DOI: 10.1002/wcc.8.CrossRefGoogle Scholar
Rust, H. W., Kruschke, T., Dobler, A., Fischer, M. and Ulbrich, U. (2015), ‘Discontinuous daily temperatures in the WATCH forcing datasets’, J. Hydrometeorol. 16(1), 465–472.CrossRefGoogle Scholar
Rust, H. W., Vrac, M., Lengaigne, M. and Sultan, B. (2010), ‘Quantifying differences in circulation patterns based on probabilistic models: IPCC AR4 multimodel comparison for the North Atlantic’, J. Climate 23(24), 6573–6589.CrossRefGoogle Scholar
Rust, H. W., Vrac, M., Sultan, B. and Lengaigne, M. (2013), ‘Mapping weather-type influence on Senegal precipitation based on a spatial–temporal statistical model’, J. Climate 26(20), 8189– 8209.CrossRefGoogle Scholar
Saji, N. H., Goswami, B. N., Vinayachand ran, P. N. and Yamagata, T. (1999), ‘A dipole mode in the tropical Indian Ocean’, Nature 401(6751), 360–363.CrossRefGoogle ScholarPubMed
Salameh, T., Drobinski, P., Vrac, M. and Naveau, P. (2009), ‘Statistical downscaling of nearsurface wind over complex terrain in southern France’, Meteorol. Atmos. Phys. 103(1), 253– 265.CrossRefGoogle Scholar
Salathé, E.P. (2005), ‘Downscaling simulations of future global climate with application to hydrologic modelling’, Int. J. Climatol. 25(4), 419–436.CrossRefGoogle Scholar
Salathé, E.P., Steed, R., Mass, C. F. and Zahn, P. H. (2008), ‘A high-resolution climate model for the U.S. Pacific Northwest: mesoscale feedbacks and local responses to climate change’, J. Climate 21, 5708–5726.CrossRefGoogle Scholar
San-Martín, D., Manzanas, R., Brand s, S., Herrera, S. and Gutiérrez, J.M. (2017), ‘Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods’, J. Climate 30(1), 203–223.CrossRefGoogle Scholar
Santer, B. (1985), ‘The use of general circulation models in climate impact analysis – a preliminary study of the impacts of CO2-induced climatic change on West European agriculture’, Clim. Change 7, 71–93.CrossRefGoogle Scholar
Satoh, M. (2013), Atmospheric circulation dynamics and general circulation models, Springer Science & Business Media.Google Scholar
Satoh, M., Matsuno, T., Tomita, H., Miura, H., Nasuno, T. and Iga, S. (2008), ‘Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud-resolving simulations’, J. Comput. Phys. 227, 3486–3514.CrossRefGoogle Scholar
Saunders, I. R. and Byrne, J. M. (1999), ‘Using synoptic surface and geopotential height fields for generating grid-scale precipitation’, Int. J. Climatol. 19(11), 1165–1176.3.0.CO;2-5>CrossRefGoogle Scholar
Sauter, T. and Venema, V. (2011), ‘Natural three-dimensional predictor domains for statistical precipitation downscaling’, J. Climate 24(23), 6132–6145.CrossRefGoogle Scholar
Scaife, A. A., Copsey, D., Gordon, C., Harris, C., Hinton, T., Keeley, S., O'Neill, A., Roberts, M. and Williams, K. (2011), ‘Improved Atlantic winter blocking in a climate model’, Geophys. Res. Lett. 38, L23703.CrossRefGoogle Scholar
Schamm, K., Ziese, M., Raykova, K., Becker, A., Finger, P., Meyer-Christoffer, A. and Schneider, U. (2015), ‘GPCC Full Data Daily Version 1.0 at 1.0?: Daily Land -
Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data', DOI: 10.5676/DWD_GPCC/FD_D_V1_100.CrossRef
Schär, C., Frei, C., Lüthi, D. and Davies, H. C. (1996), ‘Surrogate climate-change scenarios for regional climate models’, Geophys. Res. Lett. 23(6), 669–672.CrossRefGoogle Scholar
Schär, C., Lüthi, D., Beyerle, U. and Heise, E. (1999), ‘The soil-precipitation feedback: A process study with a regional climate model’, J. Climate 12(3), 722–741.2.0.CO;2>CrossRefGoogle Scholar
Schertzer, D. and Lovejoy, S. (1987), ‘Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes’, J. Geophys. Res. 92(D8), 9693–9714.CrossRefGoogle Scholar
Schiermeier, Q. (2010), ‘The real holes in climate science’, Nature 463(7279), 284–288.CrossRefGoogle ScholarPubMed
Schlesinger, M. E. and Ramankutty, N. (1994), ‘An oscillation in the global climate system of period 65–70 years’, Nature 367(6465), 723–726.CrossRefGoogle Scholar
Schmidli, J., Frei, C. and Vidale, P. L. (2006), ‘Downscaling from GCM precipitation: A benchmark for dynamical and statistical downscaling methods’, Int. J. Climatol. 26, 679–689.CrossRefGoogle Scholar
Schmidli, J., Goodess, C. M., Frei, C., Haylock, M.R., Hundecha, Y., Ribalaygua, J. and Schmith, T. (2007), ‘Statistical and dynamical downscaling of precipitation: an evaluation and comparison of scenarios for the European Alps’, J. Geophys. Res. 112(D4).CrossRefGoogle Scholar
Schmith, T. (2008), ‘Stationarity of regression relationships: Application to empirical downscaling’, J. Climate 21(17), 4529–4537.CrossRefGoogle Scholar
Schneider, S. H. (2001), ‘What is “Dangerous” climate change?’, Nature 411(6833), 17–19.CrossRefGoogle Scholar
Schneider, U., Fuchs, T., Meyer-Christoffer, A. and Rudolf, B. (2008), ‘Global precipitation analysis products of the GPCC’, Global Precipitation Climatology Centre (GPCC), DeutscherWetterdienst, Offenbach a. M., Germany, November.
Schölzel, C. and Friederichs, P. (2008), ‘Multivariate non-normally distributed rand om variables in climate research – introduction to the copula approach’, Nonlin. Proc. Geophys. 15, 761– 772.CrossRefGoogle Scholar
Schölzel, C. and Hense, A. (2011), ‘Probabilistic assessment of regional climate change in Southwest Germany by ensemble dressing’, Clim. Dynam. 36(9), 2003–2014.CrossRefGoogle Scholar
Schoof, J. T. (2013), ‘Statistical downscaling in climatology’, Geogr. Comp. 7(4), 249–265.Google Scholar
Schoof, J. T. and Pryor, S. C. (2001), ‘Downscaling temperature and precipitation: A comparison of regression-based methods and artificial neural networks’, Int. J. Climatol. 21(7), 773–790.CrossRefGoogle Scholar
Schrum, C., Hübner, U., Jacob, D. and Podzun, R. (2003), ‘A coupled atmosphere/ice/ocean model for the North Sea and the Baltic Sea’, Clim. Dynam. 21(2), 131–151.CrossRefGoogle Scholar
Schwarb, M. (2000), The alpine precipitation climate, PhD thesis, Swiss Federal Institute of Technology Zurich.
Schwarz, G. (1978), ‘Estimating the dimension of a model’, Ann. Statist. 6, 461–464.CrossRefGoogle Scholar
Schwarz, H. E. (1966), Climate, Climatic change, and water supply, National Academy of Sciences, chapter ‘Climatic Change and Water Supply: How Sensitive is the Northeast?’, pp. 111– 120.
Semenov, M. A. and Barrow, E. M. (1997), ‘Use of a stochastic weather generator in the development of climate change scenarios’, Clim. Change 35(4), 397–414.CrossRefGoogle Scholar
Semenov, M. A., Brooks, R. J., Barrow, E. M. and Richardson, C. W. (1998), ‘Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates’, Clim. Res. 10(2), 95–107.CrossRefGoogle Scholar
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B. and Teuling, A. J. (2010), ‘Investigating soil moisture – climate interactions in a changing climate: a review’, Earth Sci. Rev. 99(3), 125–161.CrossRefGoogle Scholar
Seo, K.-H., Frierson, D. M. W. and Son, J.-H. (2014), ‘A mechanism for future changes in Hadley circulation strength in CMIP5 climate change simulations’, Geophys. Res. Lett. 41(14), 5251– 5258.CrossRefGoogle Scholar
Sheffield, J., Goteti, G. and Wood, E. F. (2006), ‘Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling’, J. Climate 19(13), 3088–3111.CrossRefGoogle Scholar
Shepard, D. S. (1984), ‘Computer mapping: The SYMAP interpolation algorithm’, in Spatial statistics and models, Springer, pp. 133–145.CrossRef
Shepherd, T. G. (2014), ‘Atmospheric circulation as a source of uncertainty in climate change projections’, Nat. Geosci. 7, 703–708.CrossRefGoogle Scholar
Shindell, D., Racherla, P. and Milly, G. (2014), ‘Reply to comment by Laprise on “The added value to global model projections of climate change by dynamical downscaling: a case study over the continental US using the GISS-ModelE2 and WRF models“’, J. Geophys. Res. 119(7), 3882– 3885.Google Scholar
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W. and Bronaugh, D. (2013), ‘Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate’, J. Geophys. Res. 118(4), 1716–1733.Google Scholar
Simmons, A. J., Jones, P. D., da Costa Bechtold, V., Beljaars, A. C.M., Kållberg, P.W., Saarine, S., Uppala, S. M., Viterbo, P. and Wedi, N. (2004), ‘Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature’, J. Geophys. Res. 109(D24).CrossRefGoogle Scholar
Simmons, A. J., Willett, K. M., Jones, P. D., Thorne, P. W. and Dee, D. P. (2010), ‘Low-frequency variations in surface atmospheric humidity, temperature, and precipitation: Inferences from reanalyses and monthly gridded observational data sets’, J. Geophys. Res. 115(D1).CrossRefGoogle Scholar
Sklar, A. (1959), ‘Fonctions de répartition à n dimensions et leurs marges’, Publ. Inst. Stat. Univ. Paris 8.Google Scholar
Slingo, J., Inness, P., Neale, R., Woolnough, S. and Yang, G. (2003), ‘Scale interactions on diurnal to seasonal timescales and their relevance to model systematic errors’, Ann. Geophys. 46(1), 139–155.Google Scholar
Smith, D. M., Cusack, S., Colman, A. W., Folland, C. K., Harris, G. R. and Murphy, J. M. (2007), ‘Improved surface temperature prediction for the coming decade from a global climate model’, Science 317(5839), 796–799.CrossRefGoogle ScholarPubMed
Smith, L. A. (2002), ‘What might we learn from climate forecasts?’, Proc. Nat. Acad. Sci. 99(suppl 1), 2487–2492.CrossRefGoogle ScholarPubMed
Smith, R. L. (1990), ‘Regional estimation from spatially dependent data’, Technical report.
Soares, P. et al. (2017), ‘Process based evaluation of the VALUE perfect predictor experiment of statistical downscaling methods’, Int. J. Climatol., subm.
Solman, S. A., Sanchez, E., Samuelsson, P., da Rocha, R. P., Li, L., Marengo, J., Pessacg, N. L., Remedio, A. R.C., Chou, S., Berbery, H, Le Treut, H., de Castro, M. and Jacob, D. (2013), ‘Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties’, Clim. Dynam. 41(5- 6), 1139–1157.CrossRefGoogle Scholar
Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M. and Miller, H. L., eds. (2007), Climate change 2007: The physical science basis: Working Group I contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.Google Scholar
Somot, S., Sevault, F., Déqué, M. and Crépon, M. (2008), ‘21st century climate change scenario for the Mediterranean using a coupled atmosphere–ocean regional climate model’, Glob. Planet. Change 63(2), 112–126.CrossRefGoogle Scholar
Spearman, C. (1904), ‘“General intelligence, ” objectively determined and measured’, The American Journal of Psychology 15(2), 201–292.CrossRefGoogle Scholar
Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A., Wang, B. and Zhou, T. (2013), ‘The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century’, Clim. Dynam. 41, 2711–2744.CrossRefGoogle Scholar
Stainforth, D. A. (2016), personal communication.
Stainforth, D. A., Allen, M. R., Tredger, E. R. and Smith, L. A. (2007), ‘Confidence, uncertainty and decision-support relevance in climate predictions’, Phil. Trans. R. Soc. A 365, 2145– 2161.CrossRefGoogle ScholarPubMed
Stehlik, J. and Bárdossy, A. (2002), ‘Multivariate stochastic downscaling model for generating daily precipitation series based on atmospheric circulation’, J. Hydrol. 256(1–2), 120–141.CrossRefGoogle Scholar
Steiger, N. J., Steig, E. J., Dee, S. G., Roe, G. H. and Hakim, G. J. (2017), ‘Climate reconstruction using data assimilation of water isotope ratios from ice cores’, J. Geophys. Res. 122(3), 1545– 1568.Google Scholar
Stensrud, D. J. (2009), Parameterization schemes: keys to understand ing numerical weather prediction models, Cambridge University Press.Google Scholar
Stern, R. D. and Coe, R. (1984), ‘A model fitting analysis of daily rainfall data’, J. Roy. Stat. Soc. A 134(1), 1–34.Google Scholar
Stevens, B. and Bony, S. (2013), ‘What are climate models missing?’, Science 340(6136), 1053– 1054.CrossRefGoogle ScholarPubMed
Stocker, T. (2011), Introduction to climate modelling, Springer Science & Business Media.CrossRefGoogle Scholar
Stocker, T. F., Dahe, Q., Plattner, G.-K. and Tignor, M. (2015), ‘IPCC Workshop on Regional Climate Projections and their Use in Impacts and Risk Analysis Studies’, https://www.ipcc .ch/pdf/supporting-material/RPW_WorkshopReport.pdf.
Stocker, T. F., Qin, D., Plattner, K., Tignor, M. M.B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V. and Midgley, P. M., eds (2013), Climate change 2013: the physical science basis: Working Group I contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press.Google Scholar
Stone, M. (1977), ‘An asymptotic equivalence of choice of model by cross-validation and Akaike's criterion’, J. R. Stat. Soc. B 39, 44–47.Google Scholar
Stoner, A. M. K., Hayhoe, K., Yang, X. and Wuebbles, D. J. (2013), ‘An asynchronous regional regression model for statistical downscaling of daily climate variables’, Int. J. Climatol. 33, 2473–2494.CrossRefGoogle Scholar
Sun, F., Walton, D. B. and Hall, A. (2015), ‘A hybrid dynamical–statistical downscaling technique. Part II: End-of-century warming projections predict a new climate state in the Los Angeles region’, J. Climate 28(12), 4618–4636.CrossRefGoogle Scholar
Sutton, R. T. and Dong, B. (2012), ‘Atlantic Ocean influence on a shift in European climate in the 1990s’, Nat. Geosci. 5, 788–792.CrossRefGoogle Scholar
Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R. and Demaria, E. M.C. (2016), ‘Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes’, Hydrol. Earth Syst. Sci. accepted.Google Scholar
Takle, E. S., Gutowski, W. J., Arritt, R. A., Pan, Z., Anderson, C. J., da Silva, R. R., Caya, D., Chen, S.-C., Christensen, J. H., Hong, S.-Y., Juang, H.-M. H., J. K. W., Lapenta, Laprise, R., Lopez, P., McGregor, J. and Roads, J. O. (1999), ‘Project to Intercompare Regional Climate Simulations (PIRCS): Description and initial results’, J. Geophys. Res. 104, 19443–19461.CrossRefGoogle Scholar
Tareghian, R. and Rasmussen, P. F. (2013), ‘Statistical downscaling of precipitation using quantile regression’, J. Hydrol. 487, 122–135.CrossRefGoogle Scholar
Taylor, K. E., Stouffer, R. J. and Meehl, G. A. (2012), ‘An overview of CMIP5 and the experiment design’, Bull. Amer. Meteorol. Soc. 93, 485–498.CrossRefGoogle Scholar
Tebaldi, C. and Knutti, R. (2007), ‘The use of the multi-model ensemble in probabilistic climate projections’, Phil. Trans. R. Soc. A 365, 2053–2075.CrossRefGoogle ScholarPubMed
Tebaldi, C., Smith, R. L., Nychka, D. and Mearns, L. O. (2005), ‘Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multimodel ensembles’, J. Climate 18(10), 1524–1540.CrossRefGoogle Scholar
Teutschbein, C. and Seibert, J. (2012), ‘Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods’, J. Hydrol. 456, 12–29.Google Scholar
Themeßl, M. J., Gobiet, A. and Heinrich, G. (2012), ‘Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal’, Clim. Change 112, 449–468.CrossRefGoogle Scholar
Themeßl, M. J., Gobiet, A. and Leuprecht, A. (2011), ‘Empirical-statistical downscaling and error correction of daily precipitation from regional climate models’, Int. J. Climatol. 31, 1530–1544.Google Scholar
Thober, S., Mai, J., Zink, M. and Samaniego, L. (2014), ‘Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets’, Wat. Resour. Res. 50(11), 8714–8735.CrossRefGoogle Scholar
Thompson, D. W. J., Baldwin, M.P. and Wallace, J. M. (2002), ‘Stratospheric connection to Northern Hemisphere wintertime weather: implications for prediction’, J. Climate 15(12), 1421– 1428.2.0.CO;2>CrossRefGoogle Scholar
Thorne, P. W. and Vose, R. S. (2010), ‘Reanalysis suitable for characterizing long term trends. Are they really achievable?’, Bull. Amer. Meteorol. Soc. 91, 353–361.CrossRefGoogle Scholar
Thorne, P. and Vose, R. S. (2011), ‘Reply to comments on “Reanalyses suitable for characterizing long-term trends“’, Bull. Amer. Meteorol. Soc. 92(1), 70–72.Google Scholar
Thorne, P. W., Willett, K. M., Allan, R. J., Bojinski, S., Christy, J. R., Fox, N., Gilbert, S., Jolliffe, I., Kennedy, J. J., Kent, E., Klein Tank, A., Lawrimore, J., Parker, D. E., Rayner, N., Simmons, A., Song, L., Stott, P. A., and Trewin, B. (2011), ‘Guiding the creation of a comprehensive surface temperature resource for twenty-first-century climate science’, Bull. Amer. Meteorol. Soc. 92(11), ES40–ES47.CrossRefGoogle Scholar
Thrasher, B., Maurer, E. P., McKellar, C. and Duffy, P. (2012), ‘Technical note: bias correcting climate model simulated daily temperature extremes with quantile mapping’, Hydrol. Earth Syst. Sci. 16(9), 3309–3314.CrossRefGoogle Scholar
Tiedtke, M. (1989), ‘A comprehensive mass flux scheme for cumulus parameterization in largescale models’, Mon. Wea. Rev. 117(8), 1779–1800.2.0.CO;2>CrossRefGoogle Scholar
Tippett, M. K., DelSole, T., Mason, S. J. and Barnston, A. G. (2008), ‘Regression-based methods for finding coupled patterns’, J. Climate 21(17), 4384–4398.CrossRefGoogle Scholar
Tokmakian, R., Challenor, P. and Andrianakis, Y. (2012), ‘On the use of emulators with extreme and highly nonlinear geophysical simulators’, J. Atmos. Ocean. Tecnol. 29(11), 1704–1715.Google Scholar
Toreti, A., Kuglitsch, F. G., Xoplaki, E., Luterbacher, J. and Wanner, H. (2010), ‘A novel method for the homogenization of daily temperature series and its relevance for climate change analysis’, J. Climate 23(19), 5325–5331.CrossRefGoogle Scholar
Tran, G. T., Oliver, K. I.C., Sóbester, A., Toal, D. J.J., Holden, P. B., Marsh, R., Challenor, P. and Edwards, N. R. (2016), ‘Building a traceable climate model hierarchy with multi-level emulators’, Adv. Stat. Climatol. Meteorol. Ocean. 2(1), 17–37.Google Scholar
Trenberth, K. E. (1975), ‘A quasi-biennial stand ing wave in the Southern Hemisphere and interrelations with sea surface temperature’, Quart. J. Roy. Meteorol. Soc. 101(427), 55–74.CrossRefGoogle Scholar
Trenberth, K. E. (1992), Climate system Modeling, Cambridge University Press.Google Scholar
Trewin, B. (2010), ‘Exposure, instrumentation, and observing practice effects on land temperature measurements’, WIREs Clim. Change 1(4), 490–506.CrossRefGoogle Scholar
Trewin, B. (2013), ‘A daily homogenized temperature data set for Australia’, Int. J. Climatol. 33(6), 1510–1529.CrossRefGoogle Scholar
Underwood, F. M. (2009), ‘Describing long-term trends in precipitation using generalized additive models’, J. Hydrol. 364(3), 285–297.CrossRefGoogle Scholar
UNFCCC (1997), ‘Report of the subsidiary body for scientific and technological advice on the work of its seventh session’, FCCC/SBSTA/1997/14, United Nations, Geneva, Switzerland, 10 November.
United Nations (1992), ‘United Nations Framework Convention on Climate Change’, United Nations, 1992. FCCC/INFORMAL/84 GE.05-62220 (E) 200705. Available at www.unfccc.int.
Uppala, S. M., Källberg, P.W., Simmons, A. J. and Adrae et al., U. (2005), ‘The ERA-40 reanalysis’, Quart. J. Roy. Meteorol. Soc 131, 2961–3012.CrossRefGoogle Scholar
US Army Corps of Engineers (1971), ‘HEC-4 monthly streamflow simulation’. Hydrologic Engineering Center, Davis, California.
van Asselt, M.B.A., J. and Rotmans (2002), ‘Uncertainty in integrated assessment modelling’, Clim. Change 54(1), 75–105.CrossRefGoogle Scholar
van den Dool, H. M. (1994), ‘Searching for analogues, how long must we wait?’, Tellus A 46(3), 314–324.Google Scholar
van der Linden, P. and Mitchell, J. F.B. (2009), ‘ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project’, Technical report, Met Office Hadley Centre.
van Haren, R., van Oldenborgh, G. J., Lenderink, G., Collins, M. and Hazeleger, W. (2013a), ‘SST and circulation trend biases cause an underestimation of European precipitation trends’, Clim. Dynam. 40(1–2), 1–20.CrossRefGoogle Scholar
van Haren, R., van Oldenborgh, G. J., Lenderink, G. and Hazeleger, W. (2013b), ‘Evaluation of modeled changes in extreme precipitation in Europe and the Rhine basin’, Environ. Res. Lett. 8(1), 014053.
van Meijgaard, E., van Ulft, L. H., van de Berg, W. J., Bosveld, F. C., van den Hurk, B. J. J. M., Lenderink, G. and Sibesma, A. P. (2008), ‘The KNMI regional atmospheric climate model RACMO version 2.1, Technical Report 302, Royal Dutch Meteorological Institute, KNMI, Postbus 201, 3730 AE, De Bilt, The Netherland s.
van Oldenborgh, G. J., Doblas Reyes, F.-J., Drijfhout, S. S. and Hawkins, E. (2013), ‘Reliability of regional climate model trends’, Env. Res. Lett. 8(1), 014055.CrossRefGoogle Scholar
van Oldenborgh, G. J., Drijfhout, S., van Ulden, A., Haarsma, R., Sterl, A., Severijns, C., Hazeleger, W. and Dijkstra, H. (2009), ‘Western Europe is warming much faster than expected’, Clim. Past 5(1), 1–12.CrossRefGoogle Scholar
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J. and Rose, S. K. (2011), ‘The representative concentration pathways: an overview’, Clim. Change 109(1), 5–31.CrossRefGoogle Scholar
Vannitsem, S. (2011), ‘Bias correction and post-processing under climate change’, Nonlin. Proc. Geophys. 18, 911–924.CrossRefGoogle Scholar
Vautard, R. (1990), ‘Multiple weather regimes over the North Atlantic: analysis of precursors and successors’, Mon. Wea. Rev. 118(10), 2056–2081.2.0.CO;2>CrossRefGoogle Scholar
Vecchi, G. A., Soden, B. J., Wittenberg, A.T., Held, I. M., Leetmaa, A. and Harrison, M. J. (2006), ‘Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing’, Nature 441(7089), 73–76.CrossRefGoogle ScholarPubMed
Venema, V. K. C., Mestre, O., Aguilar, E., Auer, I., Guijarro, J. A., Domonkos, P., Vertacnik, G., Szentimrey, T., Stepanek, P., Zahradnicek, P., Viarre, J., Müller-Westermeier, G., Lakatos, M., Williams, C. N., Menne, M. J., Lindau, R., Rasol, D., Rustemeier, E., Kolokythas, K., Marinova, T., Andresen, L., Acquaotta, F., Fratianni, S., Cheval, S., Klancar, M., Brunetti, M., Gruber, C., Prohom Duran, M., Likso, T., Esteban, P. and Brand sma, T. (2012), ‘Benchmarking homogenization algorithms for monthly data’, Clim. Past 8, 89–115.CrossRefGoogle Scholar
Volosciuk, C., Maraun, D., Semenov, V. A. and Park, W. (2015), ‘Extreme precipitation in an atmosphere general circulation model: impact of horizontal and vertical model resolutions’, J. Climate 28(3), 1184–1205.CrossRefGoogle Scholar
Volosciuk, C., Maraun, D., Vrac, M. and Widmann, M. (2017), ‘A combined statistical bias correction and stochastic downscaling method for precipitation’, Hydrol. Earth Syst. Sci. 21(3), 1693–1719.CrossRefGoogle Scholar
von Storch, H. (1999), ‘On the use of “inflation” in statistical downscaling’, J. Climate 12(12), 3505–3506.2.0.CO;2>CrossRefGoogle Scholar
von Storch, H. and Hannoschöck, G. (1985), ‘Statistical aspects of estimated principal vectors (EOFs) based on small sample sizes’, J. Clim Appl. Meteorol. 24(7), 716–724.2.0.CO;2>CrossRefGoogle Scholar
von Storch, H., Langenberg, H. and Feser, F. (2000), ‘A spectral nudging technique for dynamical downscaling purposes’, Mon. Wea. Rev. 128, 3664–3673.2.0.CO;2>CrossRefGoogle Scholar
von Storch, H., Zorita, E. and Cubasch, U. (1993), ‘Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime’, J. Climate 6(6), 1161– 1171.2.0.CO;2>CrossRefGoogle Scholar
von Storch, H. and Zwiers, F. W. (1999), Statistical analysis in climate research, Cambridge University Press, Cambridge.
Vrac, M. and Friederichs, P. (2015), ‘Multivariate-intervariable, spatial, and temporal-bias correction’, J. Climate 28(1), 218–237.CrossRefGoogle Scholar
Vrac, M., Marbaix, P., Paillard, D. and Naveau, P. (2007a), ‘Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe’, Clim. Past 3(4), 669–682.CrossRefGoogle Scholar
Vrac, M. and Naveau, P. (2007), ‘Stochastic downscaling of precipitation: From dry events to heavy rainfalls’, Wat. Resour. Res. 43(7), W07402.CrossRefGoogle Scholar
Vrac, M., Stein, M. and Hayhoe, K. (2007b), ‘Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing’, Clim. Res. 34, 169–184.CrossRefGoogle Scholar
Vrac, M., Stein, M. L., Hayhoe, K. and Liang, X. Z. (2007c), ‘A general method for validating statistical downscaling methods under future climate change’, Geophys. Res. Lett. 34, L18701.CrossRefGoogle Scholar
Waldron, K. M., Paegle, J. and Horel, J. D. (1996), ‘Sensitivity of a spectrally filtered and nudged limited-area model to outer model options’, Mon. Wea. Rev. 124(3), 529–547.2.0.CO;2>CrossRefGoogle Scholar
Wallace, J. M. and Hobbs, P. V. (2006), Atmospheric science. An introductory survey, Academic Press.Google Scholar
Walton, D. B., Sun, F., Hall, A. and Capps, S. (2015), ‘A hybrid dynamical–statistical downscaling technique. Part I: Development and validation of the technique’, J. Climate 28(12), 4597–4617.CrossRefGoogle Scholar
Wang, C., Zhang, L., Lee, S.-K., Wu, L. and Mechoso, C. R. (2014), ‘A global perspective on CMIP5 climate model biases’, Nat. Clim. Change 4, 201–205.CrossRefGoogle Scholar
Ward, J. H. (1963), ‘Hierarchical grouping to optimize an objective function’, J. Amer. Stat. Assoc. 58(301), 236–244.CrossRefGoogle Scholar
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O. and Schewe, J. (2014), ‘The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework’, Proc. Nat. Acad. Sci. 111(9), 3228–3232.CrossRefGoogle ScholarPubMed
Washington, W. M. and Parkinson, C. L. (2005), An introduction to three-dimensional climate modeling, University Science Books.Google Scholar
WCRP WGRC (2014), ‘WCRP WGRC Expert Meeting on Climate Information “Distillation“’. 29–31 October 2014, Santand er, Spain.
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J. and Viterbo, P. (2014), ‘The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERAInterim reanalysis data’, Wat. Resour. Res. 50(9), 7505–7514.CrossRefGoogle Scholar
Weedon, G. P., Gomes, S., Viterbo, P., Shuttleworth, W. J., Blyth, E., Österle, H., Adam, J. C., Bellouin, N., Boucher, O. and Best, M. (2011), ‘Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century’, J. Hydrometeorol. 12(5), 823–848.CrossRefGoogle Scholar
Wehrens, R. and Buydens, L. M.C. (2007), ‘Self-and super-organizing maps in R: the Kohonen package’, J. Stat. Softw. 21(5), 1–19.CrossRefGoogle Scholar
Wheater, H. S., Chand ler, R. E., Onof, C. J., Isham, V. S., Bellone, E., Yang, C., Lekkas, D., Lourmas, G. and Segond, M.-L. (2005), ‘Spatial-temporal rainfall modelling for flood risk estimation’, Stoch. Environ. Res. Risk Assess. 19, 403–416.CrossRefGoogle Scholar
Wheater, H. S., Isham, V. S., Cox, D. R., Chand ler, R. E., Kakou, A., Northrop, P. J., Oh, L., Onof, C. and Rodriguez-Iturbe, I. (2000), ‘Spatial-temporal rainfall fields: modelling and statistical aspects’, Hydrol. Earth Syst. Sci. 4(4), 581–601.CrossRefGoogle Scholar
White, R., Cooley, D., Derby, R. and Seaver, F. (1958), ‘The development of efficient linear statistical operators for the prediction of sea-level pressure’, J. Meteorol. 15(5), 426–434.2.0.CO;2>CrossRefGoogle Scholar
White, R. H. and Toumi, R. (2013), ‘The limitations of bias correcting regional climate model inputs’, Geophys. Res. Lett. 40(12), 2907–2912.CrossRefGoogle Scholar
Whiteman, C. D. (2000), Mountain meteorology: fundamentals and applications, Oxford University Press.Google Scholar
Widmann, M. (2005), ‘One-dimensional CCA and SVD, and their relationship to regression maps’, J. Climate 18(14), 2785–2792.CrossRefGoogle Scholar
Widmann, M. and Bretherton, C. S. (2000), ‘Validation of mesoscale precipitation in the NCEP reanalysis using a new gridcell dataset for the northwestern United States’, J. Climate 13(11), 1936–1950.2.0.CO;2>CrossRefGoogle Scholar
Widmann, M., Bretherton, C. S. and Salathe, E. P. (2003), ‘Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor’, J. Climate 16(5), 799–816.2.0.CO;2>CrossRefGoogle Scholar
Widmann, M. et al. (2017), ‘Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment’, Int. J. Climatol., subm.
Widmann, M., Goosse, H., van der Schrier, G., Schnur, R. and Barkmeijer, J. (2010), ‘Using data assimilation to study extratropical Northern Hemisphere climate over the last millennium’, Clim. Past 6(5), 627–644.CrossRefGoogle Scholar
Widmann, M. and Schär, C. (1997), ‘A principal component and long-term trend analysis of daily precipitation in Switzerland’, Int. J. Climatol. 17(12), 1333–1356.3.0.CO;2-Q>CrossRefGoogle Scholar
Wigley, T. M. L., Jones, P. D., Briffa, K. R. and Smith, G. (1990), ‘Obtaining subgrid scale information from coarse-resolution general circulation model output’, J. Geophys. Res. 95, 1943– 1953.CrossRefGoogle Scholar
Wigley, T. M. L., Jones, P. D. and Kelly, P. M. (1986), The greenhouse effect. Climatic change and ecosystems, John Wiley, New York, chapter ‘Empirical Climate Studies’.Google Scholar
Wilby, R. L. (2010), ‘Opinion: evaluating climate model outputs for hydrological applications’, Hydrol. Sci. J. 55, 1090–1093.CrossRefGoogle Scholar
Wilby, R. L. (2017), Climate change in practice, Cambridge University Press.CrossRefGoogle Scholar
Wilby, R. L., Charles, S. P., Zorita, E., Timbal, B., Whetton, P. and Mearns, L. O. (2004), ‘Guidelines for use of climate scenarios developed from statistical downscaling methods’, IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA).
Wilby, R. L., Dawson, C. W. and Barrow, E. M. (2002), ‘SDSM – a decision support tool for the assessment of regional climate change impacts’, Env. Mod. Soft. 17(2), 145–157.Google Scholar
Wilby, R. L., Dawson, C. W., Murphy, C., O'Connor, P. and Hawkins, E. (2014), ‘The Statistical DownScalingModel - Decision Centric (SDSM-DC): conceptual basis and applications’, Clim. Res. 61(3), 259–276.CrossRefGoogle Scholar
Wilby, R. L. and Dessai, S. (2010), ‘Robust adaptation to climate change’, Weather 65, 180–185.CrossRefGoogle Scholar
Wilby, R. L., Friedhoff, M., Connell, R., Minikulov, N. and Leonidova, N. (2011), ‘Tajikistan Pilot Programme for Climate Resilience (PPCR) Project A4 – Improving the Climate Resilience of Tajikistan's Hydropower Sector’, Final Report.
Wilby, R. L., Hay, L. E. and Leavesley, G. H. (1999), ‘A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado’, J. Hydrol. 225(1), 67–91.CrossRefGoogle Scholar
Wilby, R. L., Hay, L. E., Gutowski, W. J., Arritt, R. W., Takle, E. S., Pan, T., Leavesley, G. H. and Clark, M. P. (2000), ‘Hydrological responses to dynamically and statistically downscaled climate model output’, Geophys. Res. Lett. 27(8), 1199–1202.CrossRefGoogle Scholar
Wilby, R. L., Tomlinson, O. J. and Dawson, C. W. (2003), ‘Multi-site simulation of precipitation by conditional resampling’, Climate Res. 23(3), 183–194.CrossRefGoogle Scholar
Wilby, R. L. and Wigley, T. M.L. (1997), ‘Downscaling general circulation model output: a review of methods and limitations’, Prog. Phys. Geogr. 21, 530–548.CrossRefGoogle Scholar
Wilby, R. L. and Wigley, T. M.L. (2000), ‘Precipitation predictors for downscaling: observed and general circulation model relationships’, Int. J. Climatol. 20(6), 641–661.3.0.CO;2-1>CrossRefGoogle Scholar
Wilby, R. L., Wigley, T. M.L., Conway, D., Jones, P. D., Hewitson, B. C., Main, J. and Wilks, D. S. (1998), ‘Statistical downscaling of general circulation model output: a comparison of methods’, Wat. Resour. Res. 34(11), 2995–3008.CrossRefGoogle Scholar
Wilcke, R. A. I., Mendlik, T. and Gobiet, A. (2013), ‘Multi-variable error correction of regional climate models’, Clim. Change 120(4), 871–887.CrossRefGoogle Scholar
Wilks, D. S. (1988), ‘Estimating the consequences of CO2-induced climatic change on North American grain agriculture using general circulation model information’, Clim. Change 13, 19–42.CrossRefGoogle Scholar
Wilks, D. S. (1998), ‘Multisite generalization of a daily precipitation generation model’, J. Hydrol. 210, 178–191.CrossRefGoogle Scholar
Wilks, D. S. (2006), Statistical methods in the atmospheric sciences, 2 edn, Academic Press/Elsevier.
Wilks, D. S. (2009), ‘A gridded multisite weather generator and synchronization to observed weather data’, Wat. Resour. Res. 45(10).CrossRefGoogle Scholar
Wilks, D. S. (2010), ‘Use of stochastic weather generators for precipitation downscaling’, WIREs Clim. Change 1(6), 898–907.CrossRefGoogle Scholar
Wilks, D. S. (2012), ‘Stochastic weather generators for climate-change downscaling, Part II: Multivariable and spatially coherent multisite downscaling’, WIREs Clim. Change 3(3), 267– 278.CrossRefGoogle Scholar
Wilks, D. S. and Wilby, R. L. (1999), ‘The weather generation game: a review of stochastic weather models’, Prog. Phys. Geogr. 23(3), 329–357.CrossRefGoogle Scholar
Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Gregersen, I. B. and Madsen, H. (2012), Impacts of climate change on rainfall extremes and urban drainage systems, IWA Publishing.
Willems, P. and Vrac, M. (2011), ‘Statistical precipitation downscaling for small-scale hydrological impact investigations of climate change’, J. Hydrol. 402(3), 193–205.CrossRefGoogle Scholar
Willett, K. M., Jones, P. D., Gillett, N. P. and Thorne, P. W. (2008), ‘Recent changes in surface humidity: development of the HadCRUH dataset’, J. Climate 21(20), 5364–5383.CrossRefGoogle Scholar
Williams, K., Brown, A., Jakob, C., Best, M., Arribas, A., Bodas-Salcedo,, A., Bony, S., Danabasoglu, G., Ebert, B., Gleckler, P., Donner, L., Miller, M., Petch, J., Scaife, A., Waliser, D. and Watanabe, M. (2013), ‘4th WGNE Workshop on Systematic Errors in Weather and Climate Models’, Workshop Summary, UK Met Office, Exeter, UK.
Winkler, J. A., Palutikof, J. P., Andresen, J. A. and Goodess, C. M. (1997), ‘The simulation of daily temperature time series from GCM output. Part II: Sensitivity analysis of an empirical transfer function methodology’, J. Climate 10(10), 2514–2532.2.0.CO;2>CrossRefGoogle Scholar
Wippermann, F. and Gross, G. (1981), ‘On the construction of orographically influenced wind roses for given distributions of the large-scale wind’, Beiträge zur Physik der Atmosphäre 54(4), 492–501.Google Scholar
Wong, G., Maraun, D., Vrac, M., Widmann, M., Eden, J. and Kent, T. (2014), ‘Stochastic model output statistics for bias correcting and downscaling precipitation including extremes’, J. Climate 27, 6940–6959.CrossRefGoogle Scholar
Wood, A. W., Leung, L. R., Sridhar, V. and Lettenmaier, D. P. (2004), ‘Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs’, Clim. Change 62(1–3), 189–216.CrossRefGoogle Scholar
Wood, A. W., Maurer, E. P., Kumar, A. and Lettenmaier, D. P. (2002), ‘Long-range experimental hydrologic forecasting for the eastern United States’, J. Geophys. Res. Atmos. 107(D20).CrossRefGoogle Scholar
Woollings, T. (2010), ‘Dynamical influences on European climate: an uncertain future’, Phil. Trans. R. Soc. A 368, 3733–3756.CrossRefGoogle Scholar
Woollings, T., Gregory, J. M., Pinto, J. G., Reyers, M. and Brayshaw, D. J. (2012), ‘Response of the North Atlantic storm track to climate change shaped by ocean-atmosphere coupling’, Nat. Geosci. 5(5), 313–317.CrossRefGoogle Scholar
World Bank (2013), ‘Turn Down the Heat: Climate Extremes, Regional Impacts, and the Case for Resilience’, a report for the World Bank by the Potsdam Institute for Climate Impact Research and Climate Analytics.
Worldbank (n.d.), ‘Climate change knowledge portal’, http://sdwebx.worldbank.org/climate portal/.
Xie, P., Chen, M. and Shi, W. (2010), ‘CPC unified gauge-based analysis of global daily precipitation’, 24th Conference on Hydrology, Atlanta, USA.Google Scholar
Xu, C. (1999), ‘From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches’, Prog. Phys. Geogr. 23(2), 229.CrossRefGoogle Scholar
Yang, C., Chand ler, R. E. and Isham, V. S. (2005), ‘Spatial-temporal rainfall simulation using generalized linear models’, Wat. Resour. Res. 41, W11415.CrossRefGoogle Scholar
Yang, G.-Y. and Slingo, J. (2001), ‘The diurnal cycle in the tropics’, Mon. Wea. Rev. 129(4), 784– 801.2.0.CO;2>CrossRefGoogle Scholar
Yarnal, B., Comrie, A. C., Frakes, B. and Brown, D. P. (2001), ‘Developments and prospects in synoptic climatology’, Int. J. Climatol. 21(15), 1923–1950.CrossRefGoogle Scholar
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N. and Kitoh, A. (2012), ‘APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges’, Bull. Amer. Meteorol. Soc. 93(9), 1401–1415.CrossRefGoogle Scholar
Yates, D., Gangopadhyay, S., Rajagopalan, B. and Strzepek, K. (2003), ‘A technique for generating regional climate scenarios using a nearest-neighbor algorithm’, Wat. Resour. Res. 39(7).CrossRefGoogle Scholar
Yee, T. W. and Wild, C. J. (1996), ‘Vector generalized additive models’, J. R. Stat. Soc. B 58, 481– 493.
Yip, S., Ferro, C. A.T., Stephenson, D. B. and Hawkins, E. (2011), ‘A simple, coherent framework for partitioning uncertainty in climate predictions’, J. Climate 24(17), 4634–4643.CrossRefGoogle Scholar
Young, K. C. (1994), ‘A multivariate chain model for simulating climatic parameters from daily data’, J. Appl. Meteorol. 33(6), 661–671.2.0.CO;2>CrossRefGoogle Scholar
Zappa, G., Shaffrey, L. C. and Hodges, K. I. (2013), ‘The ability of CMIP5 models to simulate North Atlantic extratropical cyclones’, J. Climate 26, 5379–5396.Google Scholar
Zhou, Z.-Q. and Xie, S.-P. (2015), ‘Effects of climatological model biases on the projection of tropical climate change’, J. Climate 28(24), 9909–9917.CrossRefGoogle Scholar
Zorita, E., Kharin, V. and von Storch, H. (1992), ‘The atmospheric circulation and sea surface temperature in the North Atlantic area in winter: their interaction and relevance for Iberian precipitation’, J. Climate 5(10), 1097–1108.2.0.CO;2>CrossRefGoogle Scholar
Zorita, E. and von Storch, H. (1997), ‘A survey of statistical downscaling techniques’, Technical report, GKSS report 97/E/20, GKSS Research Center: Geesthacht.
Zorita, E. and von Storch, H. (1999), ‘The analog method as a simple statistical downscaling technique: comparison with more complicated methods’, J. Climate 12(8), 2474–2489.2.0.CO;2>CrossRefGoogle Scholar
Zuidema, P., Chang, P., Medeiros, B., Kirtman, B. P., Mechoso, R., Schneider, E. K., Toniazzo, T., Richter, I., Small, R. J., Bellomo, K., Brand t, P., de Szoeke, S., Farrar, J. T., Jung, E., Kato, S., Li, M., Patricola, C., Wang, Z., Wood, R. and Xu, Z. (2016), ‘Challenges and prospects for reducing coupled climate model SST biases in the eastern tropical Atlantic and Pacific oceans: the US CLIVAR Eastern Tropical Oceans Synthesis Working Group’, Bull. Amer. Meteorol. Soc. 97, 2305–2327.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • References
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.023
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • References
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.023
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • References
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book: Statistical Downscaling and Bias Correction for Climate Research
  • Online publication: 27 December 2017
  • Chapter DOI: https://doi.org/10.1017/9781107588783.023
Available formats
×