Abstract
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 ultimate objective is to simulate local precipitation associated with altered climate regimes. Two regions, one in the Pacific-American sector (western region) and one in the American-Mid-Atlantic sector (eastern region), are explored. 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. After applying this method to historical SLP observations, precipitation simulations associated with GCM SLP output were validated in terms of probability of occurrence and survival time of the weather states identified by the CART analysis. Daily rainfall time series were then generated from weather classes derived b...
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