Abstract: A superensemble is developed using a plurality of forecasts from a variety of weather and climate models. Along with observed analysis fields, these forecasts are used to derive statistics on the past behavior of the models. These statistics, combined with future forecasts of the models, enables the construction of a superensemble forecast. More specifically, given a set of past model forecasts, the present invention uses a multiple regression technique to regress the model forecasts against observed fields. Least-squares minimization of the difference between the model and the analysis field is used to determine the weights of each model component at any geographic location and vertical level. Therefore, the superensemble generates a model that combines the historical performance of forecasting data from multiple models at a large number of geographic areas or regions.