Traditional Numerical Weather Prediction (NWP) models suffer from inherent biases in Quantitative Precipitation Forecasting (QPF) tasks due to limited spatial resolution,incomplete physical parameterization schemes,and poor generalization capabilities.Deep learning neural networks,with their robust nonlinear fitting capabilities,autonomous learning of task-specific features,and high generalization,hold the potential to address these issues and improve the current state of NWP.Here,we propose a novel short-term QPF approach based on multi-factor 3D feature extraction.Leveraging high-resolution ECMWF HRES (EC-Hres) model forecasting data provided by the European Centre for Medium Range Weather Forecasts (ECMWF),we construct a 3D-QPF semantic segmentation model.This model employs a coupled framework of classification and regression to capture the 3D spatial features of various precipitation-related element data,establishing a nonlinear relationship with actual precipitation data.Furthermore,we incorporate the PR (Precision-Recall) loss function to further enhance the model's predictive performance,particularly for skewed data.Experimental results show that the 3D-QPF model achieves a steady increase in accuracy score for daily cumulative precipitation forecast,not only at the light rain threshold (0.1 mm/(24 h)) but also significantly at the rainstorm threshold (50 mm/(24 h)),with a maximum improvement of 15.8% in TS (Threat Score) compared to that of EC-Hres and a reduction in RMSE (Root Mean Square Error) by 18.71%.Upon extended validation,the 3D-QPF model outperforms EC-Hres,China Meteorological Administration Global Model (CMA-GFS) forecasting,and classic network models such as 2D-Unet and 3D-Unet,demonstrating effective prediction correction.Notably,the model's optimization performance remains relatively stable even when the forecast lead time is extended to 72 hours.