Abstract: To improve the downscaling effectiveness of deep learning methods for daily precipitation from Global Climate Models (GCMs). Targeting the Yangtze River Basin, we constructed four deep learning downscaling models based on historical daily precipitation outputs from 20 GCMs. A hybrid method (DL-DBC) was proposed by integrating these models with the Daily Bias Correction (DBC) method. The four deep learning models exhibited comparable performance in daily precipitation downscaling. Compared to DBC, they achieved lower Mean Absolute Relative Error (MARE) for multi-year average daily precipitation but slightly higher MARE for multi-year average monthly and annual precipitation. The DL-DBC method outperformed standalone deep learning models, reducing MARE for multi-year average annual precipitation by 6.7%–11.3% and monthly precipitation by 6.3%–7.6%, while also demonstrating superior performance in precipitation frequency analysis. The DL-DBC method enhances the downscaling effectiveness of deep learning models and further reduces biases in on daily precipitation data from GCMs.