Accurate hourly precipitation forecasting is essential for local socioeconomic activities. Here, a U-shaped neural network (U-Net) with a customized loss function for precipitation is proposed to enhance 1–24-hour precipitation post-processing over Beijing, China. 36 precipitation-related predictors, informed by local forecasters’ expertise and grounded in physical constraints, are utilized as inputs. Permutation importance (PI) and saliency maps are used to evaluate the average and specific influence of selected predictors on U-Net predictions. The findings indicate the inclusion of additional predictors is crucial for bias correction, resulting in improvements of ~14.3\% in critical success index for precipitation exceeding 10mm/h. PI analysis reveals that associated atmospheric variables function as important supplementary indicators, and saliency maps visualizations suggest that key region influencing a given grid-point extends to ~30 kilometers for 1–24-hour precipitation forecasting. It demonstrates that integrating knowledge-based predictors is promise in advancing precipitation post-processing, and further understanding and interpretation of model is crucial.