Subseasonal precipitation prediction over East Asia using an adaptive bias correction model: methodology and evaluation

Abstract Accurate and reliable subseasonal precipitation forecasts are critical for disaster prevention and mitigation, particularly in densely populated regions like East Asia. However, substantial gaps remain between the reliability and accuracy of dynamical model forecasts and societal demands. This study proposes a machine learning-based adaptive bias correction (ABC) method to postprocess forecasts from the Climate Forecast System version 2 (CFS) and the European Centre for Medium-Range Wea