A Segmented Classification and Regression Machine Learning Approach for Correcting Precipitation Forecast at 4–6 H Leadtimes

Accurate and fine-scale short-term precipitation forecasting is crucial for disaster prevention, mitigation, and socioeconomic development. Currently, the direct precipitation forecasts of numerical weather prediction often face great challenges and correction methods are still needed to further improve the forecast accuracy. By utilizing the 500-m resolution fusion precipitation data from the Rapid-refresh Integrated Seamless Ensemble (RISE) system in the Beijing–Tianjin–Hebei (BTH) region, this study proposes a new Segmented Classification and Regression machine learning model based on the extreme gradient boosting (XGBoost) algorithm, termed SCR-XGBoost, which can be applied to correct hourly precipitation forecasts in areas with a dense network of weather stations at lead times of 4–6 h. The performance of the model is evaluated according to six metrics: the accuracy (AC), mean absolute error (MAE), root mean square error (RMSE), correlation coefficient (CC), threat score (TS), and bias score (BS). The results indicate that, although the XGBoost algorithm is almost ineffective for directly forecasting precipitation, the SCR-XGBoost model can significantly improve the forecast performance compared with the original RISE forecast, and the segmented correction method for torrential rainfall (≽ 20 mm h−1) outperforms other precipitation grades, which can effectively alleviate the problem of false alarms in the RISE system for heavy rainfall and above (≽ 10 mm h−1). The optimization rates after applying the SCR-XGBoost model correction in precipitation forecasts can be improved by 6.49