AI-Refined Ensemble Precipitation and Temperature Projections for Regional Impact Assessment over the Conterminous United States

Abstract

Abstract Access to high-resolution long-term earth system projections is essential for advancing research in hydrology, agriculture, disaster management, and adaptation planning. This Data Descriptor presents an Artificial Intelligence (AI)-refined ensemble of Earth system model (ESM) projections for the conterminous United States at 1/24° (~4 km) spatial resolution. The dataset was generated by downscaling ten Coupled Models Intercomparison Project phase 6 (CMIP6) Earth system models (ESMs) under two emissions scenarios (SSP245 and SSP585) for an 80-year period (1980–2059). Two AI-driven methods, Super-Resolution Convolutional Neural Networks (SRCNN) and Super-Resolution Generative Adversarial Networks (SRGAN), were applied to produce high-resolution daily precipitation and minimum/maximum temperature. This descriptor documents the input datasets, processing workflow, bias-correction steps, file structure, variables, spatial and temporal coverage, and technical validation of the released data. Validation analyses compare the generated products with Daymet observations and existing downscaled datasets to characterize spatial patterns, biases, temporal consistency, and differences among products. The dataset provides daily high-resolution historical and future earth system projections that can support regional, impact assessment, and related applications, and details the AI downscaling framework, training protocols, and evaluation strategies to ensure reproducibility and usability.