Weather Emulators at the Frontier of Heat Extremes Predictability
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge. Here we evaluate six state-of-the-art deep learning weather emulators - Pangu-Weather, FuXi, ArchesWeather, AIFS, GraphCast and Aurora - alongside leading dynamical systems and statistical baselines in forecasting global near-surface temperature and extreme heat at lead times of 10-15 days. We find that several emulators rival or even surpass physics-based forecasts in deterministic temperature skill, but do so at...
| Authors | Cas Decancq, Thomas Mortier, Jessica Keune et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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