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Subseasonal forecasting -- predicting temperature and precipitation 2 to 6
weeks 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 remain poor, partly due to
stubborn errors in representing atmospheric dynamics and physics inside
dynamical models. Here, 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. We show that, 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 to explain ABC skill gains
and identify higher-skill windows of opportunity based on specific climate
conditions.
