Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme Precipitation Over Southern China Based on a Deep Learning Method

Abstract

Abstract The reliable Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, particularly extreme precipitation, is critical for disaster prevention and mitigation, which however remains a great challenge for mission agencies and research communities. In this study, a deep learning method based on U‐Net with additional atmospheric factor forecasts included is proposed to improve S2S quantitative forecasts of summer precipitation over Southern China. The weighted loss function integrated by mean square error and threat score is introduced to capture extreme precipitation more precisely. Generally, the U‐Net model shows promising results in both general statistics and extreme events. Predictor importance analyses show that the U‐Net forecast skills at the 1‐week lead time mainly arise from synchronous precipitation forecasts, but the contributions made by atmospheric factor forecasts rise rapidly with increasing lead times. Therefore, the channel combining numerical weather prediction model and deep learning framework is demonstrated promising in S2S precipitation forecasts.