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Abstract

Two statistical approaches for linking large-scale atmospheric circulation patterns and daily local rainfall are applied to GCM (general circulation model) climate simulations. The first method is based on Classification and Regression Trees (CART) analysis. The CART method classifies observed daily sea level pressure (SLP) fields into weather types that are most strongly associated with the presence/absence of rainfall at selected index stations. While the mean rainfall and probability distributions were rather well replicated, the precipitation generator based on this version of the CART technique had two important deficiencies: the generated dry periods were too short, on average, and the identification of weather states may be not invariant under coordinate rotations. The second rainfall generator is based on the analog method and uses information about the evolution of the SLP field from several previous days. It considers a pool of past observations for the circulation patterns closest to the target circulation. It is similar to the CART method and in certain aspects it performs better. -from Authors