A Machine Learning-Based Tropical Cyclone Precipitation Simulation in China

Heavy precipitation is a major hazard associated with tropical cyclones, often causing substantial economic losses and casualties through secondary disasters such as floods, landslides, and debris flows. The southeastern coast of China is one of the region most severely impacted by tropical cyclones. Under the context of global warming, the risks posed by tropical cyclone precipitation are expected to increase further. Accurate simulation of tropical cyclone rainfall is crucial for assessing flood hazards and provides a scientific basis for regional disaster risk mitigation policies. In this study, based on MSWEP precipitation data and tropical cyclone track data, we developed a China-focused tropical cyclone precipitation simulation model using the XGBoost algorithm reconstructed the precipitation field of TCs from 2000~2020. First, based on the tropical cyclone best-track data provided by the China Meteorological Administration, a rainfall field was constructed as a collection of 100 km × 100 km grid cells, forming an approximately circular domain with a radius of about 1000 km centered on the tropical cyclone. Mean precipitation for each grid cell was then extracted from the MSWEP dataset. Fifteen predictor variables were selected, including cyclone center latitude and longitude, grid center latitude and longitude, distance and azimuth between grid center and cyclone center, elevation, slope, aspect, wind speed and direction, cyclone forward direction, distance to land, season, and whether the cyclone center was over land. Based in MSWEP data from 2000 to 2020, a model was trained to predict precipitation in each grid using XGBoost algorithm. Based on this model, a reconstructed dataset of tropical cyclone rainfall for 2000–2020 was generated and evaluated. The main results indicate that, for a 70:30 train-test split, the model achieved RMSE=173.768mm, MAE= 85.504mm, and R²=0.674, demonstrating good performance. The simulated data effectively reproduce the spatial distribution of total tropical cyclone precipitation. Comparison of precipitation distribution maps based on MSWEP and simulated data further confirms that the model captures the spatial characteristics of total tropical cyclone rainfall with reasonable accuracy.