Mapping Rain-Snow Partitioning Accuracy Across China's Diverse Climate Zones: Conventional and Machine Learning Methods

Based on surface observation data from China for the period 1961–1979, this study systematically evaluates the rain/snow partitioning performance of eight conventional physical threshold schemes (Ta, Td, Tw, Ding, Trh, Dai, Sigmoid, and Yamazaki) and six machine learning models (Logistic Regression (LR), Random Forest (RF), Multi-Layer Perceptron (MLP), XGBoost, LightGBM, and CatBoost) across diverse geographical settings and within specific ranges of temperature (−10 to 10 °C), relative humidity (0 to 1), and elevation (0 to 5 km), employing a comprehensive multi-metric evaluation framework that includes the F1 score, Heidke Skill Score (HSS), overall accuracy (OA), and overestimation error (OE).