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There are a number of statistical techniques that downscale coarse climateinformation from general circulation models (GCMs). However, many of them donot reproduce the small-scale spatial variability of precipitationexhibited by the observed meteorological data, which is an important factorfor predicting hydrologic response to climatic forcing. In this study a newdownscaling technique (Bias-Correction and Stochastic Analog method; BCSA)was developed to produce stochastic realizations of bias-corrected daily GCMprecipitation fields that preserve both the spatial autocorrelation structureof observed daily precipitation sequences and the observed temporal frequencydistribution of daily rainfall over space.  We used the BCSA method to downscale 4 different daily GCM precipitationpredictions from 1961 to 1999 over the state of Florida, and compared theskill of the method to results obtained with the commonly usedbias-correction and spatial disaggregation (BCSD) approach, a modifiedversion of BCSD which reverses the order of spatial disaggregation andbias-correction (SDBC), and the bias-correction and constructed analog(BCCA) method. Spatial and temporal statistics, transition probabilities,wet/dry spell lengths, spatial correlation indices, and variograms for wet(June through September) and dry (October through May) seasons werecalculated for each method.  Results showed that (1) BCCA underestimated mean daily precipitation for bothwet and dry seasons while the BCSD, SDBC and BCSA methods accuratelyreproduced these characteristics, (2) the BCSD and BCCA methodsunderestimated temporal variability of daily precipitation and thus did notreproduce daily precipitation standard deviations, transition probabilitiesor wet/dry spell lengths as well as the SDBC and BCSA methods, and (3) theBCSD, BCCA and SDBC methods underestimated spatial variability in dailyprecipitation resulting in underprediction of spatial variance andoverprediction of spatial correlation, whereas the new stochastic technique(BCSA) replicated observed spatial statistics for both the wet and dryseasons. This study underscores the need to carefully select a downscalingmethod that reproduces all precipitation characteristics important for thehydrologic system under consideration if local hydrologic impacts of climatevariability and change are going to be reasonably predicted. For low-relief,rainfall-dominated watersheds, where reproducing small-scale spatiotemporalprecipitation variability is important, the BCSA method is recommended foruse over the BCSD, BCCA, or SDBC methods

Development and comparative evaluation of a stochastic analog method to downscale daily GCM precipitation