Abstract: As an important approach to improving prediction accuracy, the post-process error correction of climate model products plays an indispensable role in global operational climate systems. To enhance the prediction precision of numerical climate prediction models, this study applies the convolutional neural network (CNN) approach to conduct post-process correction on key operational prediction products of the third-generation climate operational prediction system of the China Meteorological Administration (CMA), i.e., CMA-CPSv3. The targeted products include monthly 2-meter air temperature, precipitation over China, and the El Niño-Southern Oscillation (ENSO) index during the period 2001—2023. Using reanalysis data from the National Centers for Environmental Prediction (NCEP) as the observational benchmark, a dedicated correction model has been developed through deep learning training of a multi-layer CNN architecture. After model construction, changes in the model performance before and after correction are evaluated during an independent test period. Results indicate that the CNN model significantly improves the prediction accuracy of climate model products. For temperature and precipitation predictions in China, the correlation coefficient of 1—7 months lead predictions is increased by 0.1—0.5. Among these improvements, the root mean square error (RMSE) of temperature is decreased by 0.5—1℃, representing a reduction rate of 20%—30%. For precipitation, the correlation coefficient is increased by 0.1—0.2 (an increase of 10%—20%), and the RMSE is decreased by 0.1—1 mm/d (a reduction rate of 3%—30%), with the RMSE reduction rate reaching 30%—50% in Eastern and Southeastern China. For the ENSO index, the correlation skill for forecasts with a lead time of 1—7 months is enhanced by 5%—7%, and the RMSE at a lead time of 7 months is reduced by 50%, suggesting that the model effectively addresses the issue of excessive oscillation amplitude of the ENSO index in the original CMA-CPSv3 model. Furthermore, this study explicitly identifies a limitation of the CNN model, i.e., excessive intensity smoothing, when applied to the correction of extreme climate events, and proposes multi-dimensional directions for future optimization. It thus provides a technical solution that integrates scientific rigor and practical applicability for operational post-processing of CMA's climate models.