UT-GraphCast Hindcast Dataset: A Global AI Forecast Archive from UT Austin for Weather and Climate Applications

Executive Summary The UT-GraphCast Hindcast Dataset (1979-2024) is a comprehensive global weather forecast archive generated using the Google DeepMind GraphCast Operational model. Developed by researchers at The University of Texas at Austin and published under the WCRP umbrella, this dataset provides daily 15 day deterministic forecasts at 00 UTC on a 0.25 0.25 global grid ( 25 km) for a 45-year period. It predicts more than a dozen key atmospheric and surface variables on 37 vertical levels, delivering a full medium-range forecast in under one minute on modern hardware. This new hindcast archive enables retrospective studies of historical weather, climate variability, and extreme events with unprecedented spatial and temporal detail. Preliminary validation shows that GraphCast forecasts generally reproduce ERA5 conditions with high fidelity and skill comparable or superior to conventional numerical models up to 10-15 days. In particular, GraphCast is known to outperform the state-of-the-art ECMWF IFS High-Resolution model (HRES) [Lam et al., 2023] on most verification targets, and to predict severe events (e.g., tropical cyclones, atmospheric rivers, heatwaves) with excellent accuracy. These benchmarks suggest that the GraphCast hindcast will be a valuable tool for climate and weather research.