Abstract: In view of the problems such as insufficient precipitation data due to the difficulties in deployment of the lake meteorological observation station and the complexity of rainfall calculation under the traditional numerical model， this paper takes Chaoahu Lake as the research object， and creates a model dataset using radar 3D mosaic data and precipitation data from meteorological observation station in the Chaohu Lake basin. Then a quantitative precipitation estimation （QPE） model based on ensemble learning is constructed， and the self-made data set is used to train the model. Combined with geographic information system （GIS）， the Chaohu Lake basin is divided by latitude and longitude grid and superimposed on the radar mosaic spatially. The arithmetic mean of the rainfall at each grid point which is calculated by the QPE model is calculated to obtain the rainfall on the lake surface. In the study， the performance of the QPE model based on ensemble learning of random forest （RF）， XGBoost and LightGBM algorithms is compared and analyzed， and the QPE model with better performance is selected to conduct hyperparameter tuning. And the results of lake surface rainfall obtained by grid averaging method and CMA land data assimilation system （CLDAS） are comparatively analyzed. The results show that the QPE model using RF algorithm has better performance， and the overall trend is consistent although there are differences in the results values calculated by the grid average method and CLDAS. In conclusion， this method could be used to calculate the surface rainfall of Chaohu Lake， and provides an important reference for flood prevention and control of Chaohu Lake and its watershed.

Key words: ensemble learning, quantitative precipitation estimation, GIS, areal rainfall, Chaohu Lake