Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

TL;DR

This paper introduces probabilistic bias correction (PBC), a machine learning framework that significantly enhances subseasonal weather forecast accuracy by reducing systematic errors in dynamical and AI models.

Contribution

The authors develop and demonstrate PBC, a novel machine learning approach that doubles the skill of AI forecasts and improves dynamical models for subseasonal weather prediction.

Findings

PBC doubles the skill of AI forecasting systems.

PBC improves the skill of operational dynamical models for most weather targets.

ECMWF's global forecasts with PBC ranked first in 2025 real-time competition.

Abstract

Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors and persistent biases. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic forecasts. When applied to the leading dynamical and AI models from the European Centre for Medium-Range Weather Forecasts (ECMWF), PBC doubles the subseasonal skill of the AI Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. We designed PBC for operational deployment, and, in ECMWF's 2025 real-time forecasting competition, its global forecasts placed first for all weather variables and lead times, outperforming the dynamical models from six operational forecasting centers, an international dynamical multi-model ensemble, ECMWF's AI Forecasting System, and the forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning, energy management, and disaster preparedness in vulnerable communities.