Extended-range forecasting (i.e.,weather predictions with lead times of 10—30 days) remains a major challenge in meteorology.This study utilizes daily precipitation data from 97 meteorological stations in Hunan Province from 2005 to 2022,along with subseasonal-to-seasonal (S2S) forecast products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP).The dataset is divided into a training and validation period (2005—2018) and an independent prediction period (2019—2022) to investigate extended-range precipitation forecasting.To address the systematic biases inherent in numerical models for extended-range forecasting,this study proposes a two-stage correction scheme.In the first stage,a hierarchical cumulative probability matching method is employed to adjust the forecast distribution and reduce systematic errors.In the second stage,a low-frequency threshold method is applied to further refine the forecasts,particularly for extreme precipitation events.Building upon this bias-corrected dataset,a convolutional neural network (CNN) model is developed to capture the coupling relationships between large-scale circulation patterns and precipitation fields.The CNN models are trained separately using the ECMWF and NCEP forecast products.Experimental results show that bias correction significantly improves the accuracy of extended-range precipitation forecasts for both models,with the NCEP model exhibiting greater improvement.Specifically,the threat score (TS) for single-station precipitation forecasts from the NCEP model increases by 38.5％,while its regional precipitation score improves by 43.9％.In comparison,the TS for the ECMWF model improves by 14.0％,and its regional precipitation score increases by 24.2％.Independent predictions indicate that the ECMWF model generally outperforms the NCEP model,particularly before for lead times of less than 15 days,although the performance gap narrows as the lead time increases.Furthermore,CNN-based models demonstrate superior forecasting skill compared to standalone numerical models,particularly for lead times of 15—30 days,where their advantages become more pronounced.In long-term extended-range precipitation forecasting,the CNN model,trained on historical data,effectively identifies precursor signals of precipitation,thereby significantly improving forecast accuracy.To further optimize forecasting performance,an integrated prediction approach is proposed.This method combines the corrected numerical model forecasts with the CNN-based forecasts using a weighted integration technique,balancing the strengths of each model while minimizing errors.The integrated forecasts exhibit high predictive skill across the entire 10—30 days extended-range period,providing a reliable basis for operational precipitation forecasting.Future research will focus on extending the model to regions with diverse climatic conditions,exploring more advanced machine learning techniques,and incorporating additional predictive factors closely related to precipitation.These efforts aim to enhance the model’s generalizability and forecasting precision.