Customized downscaling and bias-correction of future precipitation from General Circulation Models (GCMs) are needed for use in evaluating climate change impact on regional water supply sources. This study extends an exhaustive performance analysis of models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), focusing on their proficiency in simulating historical precipitation and surface air temperature. Using the Bias Correction and Stochastic Analog (BCSA) method, a sub-set of CMIP6 GCMs were subjected to further downscaling and bias correction. The study drew upon historical precipitation data, at 1/8-degree longitude by 1/8-degree latitude, from the Florida Peninsula spanning 1981-2010 as a reference for establishing spatial correlations across varied locations. The model outputs were identified for four distinct 30-year future periods: the 2030s (2020–2049), 2050s (2040–2069), 2070s (2060–2089), and 2080s (2070–2099). An enhanced BCSA method was utilized on gridded precipitation data for each 30-year future period, leading to the development of future precipitation datasets. The study then explored precipitation characteristics, such as seasonal averages and variability, along with the frequency and intensity of extreme precipitation events. These findings will be instrumental in evaluating the potential impact of climate change on the water supply sources of the Tampa Bay region.
