Machine-Learning Classification of Next-Day Extreme Precipitation in Guangzhou

This study retrospectively evaluates one-day-ahead classification of daily extreme precipitation in Guangzhou. Seven consecutive days of ERA5 atmospheric fields over a 41×65 regional grid are used to predict whether precipitation on the following day exceeds the 95th percentile of training-period daily IMERG precipitation. A no-event baseline, logistic regression, random forest, and a convolutional long short-term memory network (ConvLSTM) are compared with a chronological 70%/15%/15% split. On the held-out test period, ConvLSTM attained the highest recall (0.586), F1 score (0.347), receiver operating characteristic area under the curve (0.888), and Critical Success Index (0.210), detecting 34 of 58 extreme days. It also generated 104 false alarms and had a precision of 0.246. The observed results therefore show a detection–false-alarm tradeoff rather than an operationally reliable warning system. Because the comparison changes both model architecture and feature representation, and because it uses one temporal split without uncertainty intervals, the results do not isolate the causal benefit of spatial learning or establish statistical superiority. The study provides a reproducible framework for further evaluation, calibration, and physically informed analysis of extreme-precipitation forecasts in the Pearl River Delta.