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Global medium-range weather forecasting is critical to decision-making acrossmany social and economic domains. Traditional numerical weather prediction usesincreased compute resources to improve forecast accuracy, but cannot directlyuse historical weather data to improve the underlying model. We introduce amachine learning-based method called "GraphCast", which can be trained directlyfrom reanalysis data. It predicts hundreds of weather variables, over 10 daysat 0.25 degree resolution globally, in under one minute. We show that GraphCastsignificantly outperforms the most accurate operational deterministic systemson 90% of 1380 verification targets, and its forecasts support better severeevent prediction, including tropical cyclones, atmospheric rivers, and extremetemperatures. GraphCast is a key advance in accurate and efficient weatherforecasting, and helps realize the promise of machine learning for modelingcomplex dynamical systems.

GraphCast: Learning skillful medium-range global weather forecasting