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.

翻译：决策者依赖天气预报来种植作物、管理野火、调配水资源与能源，并应对极端天气事件。如今，得益于基于物理的动力模型与数据驱动的人工智能（AI）模型的持续进步，这些预报在两周内的准确性达到前所未有的水平。然而，在次季节时间尺度（提前2-6周）上，由于累积误差和持续偏差，模型技能急剧下降。为了应对这一退化，我们引入了概率偏差校正（PBC）这一机器学习框架，通过学习校正历史概率预报，大幅减少系统性误差。当应用于欧洲中期天气预报中心（ECMWF）领先的动力模型和AI模型时，PBC将AI预报系统的次季节技能提升了一倍，并改善了经过业务偏差校正的动力模型在91%的气压目标、92%的温度目标以及98%的降水目标上的技能。我们设计的PBC适用于业务部署，在ECMWF 2025年实时预报竞赛中，其全球预报在所有天气变量和预报时效上均排名第一，战胜了来自六个业务预报中心的动力模型、一个国际动力多模型集合、ECMWF的AI预报系统以及全球34个团队的预报系统。这些概率技能的提升转化为更准确的极端事件预测，并有可能改善脆弱社区的农业规划、能源管理和灾害防备能力。