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Abstract This study presents the spatial interactions downscaling (SPID) method and introduces the climate data for adaptation and vulnerability assessments (ClimAVA) dataset. SPID employs random forest models to capture the relationship between spatial patterns at global circulation model (GCM) resolution and fine-resolution pixel values. In summary, a random forest model is trained for each fine spatial resolution pixel of the reference data as the predictand, and nine pixels from the spatially resampled (coarser) version of the reference data at the GCM’s resolutions as predictors. Models are then utilized to downscale the bias-corrected GCM data. The ClimAVA-SW dataset offers a high-resolution (4 km), bias-corrected, downscaled future climate projection derived from seventeen CMIP6 GCMs. It includes three variables (daily precipitation, minimum and maximum temperature) for three shared socioeconomic pathways (SSP245, SSP370, SSP585) across the U.S. Southwest region. The ClimAVA dataset sets itself apart with the SPID method’s capacity to provide remarkable climate realism, high physical plausibility of change, and excellent representation of extreme events while maintaining user-friendliness and requiring relatively low computational resources. |
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