Subseasonal forecasting $\unicode{x2013}$ predicting temperature and precipitation 2 to 6 weeks ahead $\unicode{x2013}$ is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remains poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. To counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% (over baseline skills of 0.18-0.25) and precipitation forecasting skill by 40-69% (over baseline skills of 0.11-0.15) in the contiguous U.S. We couple these performance improvements with a practical workflow, based on Cohort Shapley, for explaining ABC skill gains and identifying higher-skill windows of opportunity based on specific climate conditions.

翻译：次季节预测——提前2至6周预测温度和降水——对有效水资源分配、野火管理以及干旱和洪水缓解至关重要。近期国际研究努力已提升了业务动力模式的次季节预测能力，但温度和降水预测技能仍然较差，部分原因是动力模式内部在表征大气动力学和物理过程时存在顽固误差。为应对这些误差，我们提出一种自适应偏差修正方法，该方法结合最先进的动力预报与观测数据，并利用机器学习技术。当应用于欧洲中期天气预报中心（ECMWF）的领先次季节模式时，ABC方法在美国本土将温度预测技能提升了60-90%（基准技能为0.18-0.25），降水预测技能提升了40-69%（基准技能为0.11-0.15）。我们还将这些性能提升与基于Cohort Shapley的实用工作流程相结合，用于解释ABC的技能增益，并基于特定气候条件识别更高技能的“机遇窗口”。