The ClimAVA-SWE dataset provides bias-corrected daily snow water equivalent (SWE) estimates at approximately 4 km spatial resolution across the western United States, publicly available through the Harvard Dataverse and can be accessed via its official repository at <a href="https://doi.org/10.7910/DVN/SCD2VT" target="_blank" rel="noopener">https://doi.org/10.7910/DVN/SCD2VT</a> (Khoshnood Motlagh, de Lima Moraes and Smith, 2026). The dataset is generated using the Spatial Interactions Downscaling for Snow Water Equivalent (SPID-SWE) method, a data-driven statistical downscaling framework that integrates high-resolution reference SWE data (NSIDC-0719) with daily outputs from an ensemble of 14 CMIP6 global climate models (GCMs). SPID-SWE employs a dual random forest modeling strategy that explicitly distinguishes snow accumulation and ablation phases, improving seasonal SWE representation relative to single-phase approaches. ClimAVA-SWE spans a historical period (1981–2014) and future projections (2015–2100) under three Shared Socioeconomic Pathways (SSP245, SSP370, and SSP585). The pixel-based design of SPID-SWE independently downscales each grid cell across space and time, preserving both spatial and temporal variability in the SWE signal. Although performance is conditioned on the quality of the driving input data, the dataset demonstrates strong accuracy and computational efficiency, providing a robust and scalable resource for high-resolution climate and hydrological impact applications.