The ClimAVA_SWE data set — where ClimAVA stands for Climate Data for Adaptation and Vulnerability Assessments — provides high-resolution (4 km) future climate projections derived from 13 CMIP6 General Circulation Models (GCMs). It focuses on Snow Water Equivalent (SWE), a crucial indicator of water availability, hydrologic extremes, and climate-related vulnerability, and includes projections for three Shared Socioeconomic Pathways (SSP245, SSP370, and SSP585) at a daily temporal scale. The initial release of ClimAVA_SWE covers the entire western United States. ClimAVA_SWE is produced using the newly developed Spatial Interactions Downscaling (SPID) method, which ensures high-quality downscaling through advanced machine learning techniques. SPID captures the relationship between large-scale spatial patterns at GCM resolution and fine-scale pixel values. For each pixel, two Random Forest models (one for the accumulation period and one for the ablation period) were trained using fine-resolution reference data as the predictand, and nine neighboring pixels from a spatially resampled (coarser) version of the reference data as predictors. These trained models are then applied to bias-corrected GCM data to generate the downscaled projections. The resulting dataset maintains strong climate realism and effectively represents extreme events.

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.