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Stochastic models of daily precipitation are useful both for characterizing different pre- cipitation climates and for stochastic simulation of these climates in conjunction with agricultural, hydrological, or other response models. A simple stochastic precipitation model is used to downscale— i .e . disaggregate from area-average to individual station— precipitation statistics for 6 groups of 5 U.S. stations, in a way that is consistent with observed relationships between the area-averaged series and their constituent station series. Each group of stations is located within a General Circulation Model grid-box-sized area, and collectively they exhibit a broad range of precipitation climates. The down- scaling procedure is validated using natural climate variability in the observed precipitation records as an analog for climate change, by alternately considering collections of the driest and wettest seasons as 'base' and 'future' climates, and comparing the 2 sets of downscaled station parameters to those fit directly to the respective withheld observations. The resulting downscaled stochastic model parame- ters can be readily used for local-scale simulation of climate-change impacts.

Multisite downscaling of daily precipitation with a stochastic weather generator