Accurate prediction of thunderstorms during the pre-monsoon season (April-June) in India is essential for human activities such as construction, aviation and agriculture. Two objective forecasting methods are developed using data from May and June for 1985–89. The developed methods are tested with independent data sets of the recent years, namely May and June for the years 1994 and 1995. The first method is based on a graphical technique. Fifteen different types of stability index are used in combinations of different pairs. It is found that Showalter index versus Totals total index and Jefferson's modified index versus George index can cluster cases of occurrence of thunderstorms mixed with a few cases of non-occurrence along a zone. The zones are demarcated and further sub-zones are created for clarity. The probability of occurrence/non-occurrence of thunderstorms in each sub-zone is then calculated. The second approach uses a multiple regression method to predict the occurrence/non-occurrence of thunderstorms. A total of 274 potential predictors are subjected to stepwise screening and nine significant predictors are selected to formulate a multiple regression equation that gives the forecast in probabilistic terms. Out of the two methods tested, it is found that the multiple regression method gives consistently better results with developmental as well as independent data sets; it is a potential method for operational use.