Subseasonal forecasting \unicode{x2013} predicting temperature and
precipitation 2 to 6 weeks \unicode{x2013} ahead 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% and precipitation forecasting skill by 40-69% 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.Comment: 16 pages of main paper and 2 pages of appendix tex