Soukayna Mouatadid

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.

A machine-learning postprocessing layer that learns to correct errors in existing forecasting models doubles the accuracy of a gold-standard model over the two-to-six-week subseasonal range and, for the first time, consistently beats climatological averages.

Subseasonal forecasts, two to six weeks ahead, have long hovered near the climatological average because errors compound, and operational dynamical ensembles and even state-of-the-art probabilistic AI routinely underperform climatology at longer subseasonal leads. Probabilistic bias correction uses machine learning to fix the forecast distribution itself rather than only a single point estimate. It doubles the subseasonal skill of an AI forecasting system and beats the operationally debiased dynamical model for 91% of pressure, 92% of temperature and 98% of precipitation targets; in the 2025 real-time competition its global forecasts ranked first for all variables and lead times.

Subseasonal forecasts, two to six weeks ahead, have long hovered near the climatological average because errors compound, and operational dynamical ensembles and even state-of-the-art probabilistic AI routinely underperform climatology at longer subseasonal leads. Probabilistic bias correction uses machine learning to fix the forecast distribution itself rather than only a single point estimate. It doubles the subseasonal skill of an AI forecasting system and beats the operationally debiased dynamical model for 91% of pressure, 92% of temperature and 98% of precipitation targets; in the 2025 real-time competition its global forecasts ranked first for all variables and lead times.