aceAdvancing subseasonal forecasting with machine learning
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy and prepare for weather extremes1-3. Today, such forecasts enjoy unprecedented accuracy as far as 2 weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence models4-10. However, model skill drops precipitously at subseasonal timescales (2-6 weeks ahead) due to compounding errors, systemic model biases and the chaotic nature of the atmosphere11-15. To counter this…
A machine learning bias-correction layer doubled the subseasonal skill of ECMWF's AI model and beat leading operational and team-built systems in a 2025 real-time competition.
The benefits for agriculture, energy management and disaster preparedness in vulnerable communities are described only as potential, not as demonstrated outcomes.
Evidence
- Peer-reviewedNature2026-10-14
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Truvace Impact Record TRV-2026-1465, v1: “Advancing subseasonal forecasting with machine learning.” Truvace, 2026-10-15. /record/TRV-2026-1465 (accessed at citation time). sha256 d112a7169eb0ca93…
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