Abstract: Skillful Seasonal prediction of summer precipitation in monsoon regions is crucial for disaster prevention and mitigation, and it is also a difficult and hot spot in climate prediction. In this study, we developed the IAP-DCPv3.5(Institute of Atmospheric Physics dynamical climate prediction system version 3.5) dynamical climate prediction system based on the land-atmosphere coupled model component of the second-generation Chinese Academy of Sciences Earth System Model (CAS-ESM2.0). Specifically, we designed a land surface initialization scheme compatible for Common Land Model (CoLM) and incorporated a stepwise regression-based bias correction method for precipitation prediction. Utilizing a 30-year ensemble hindcast experiment results from 1991 to 2020, we evaluated the system"s seasonal prediction skill for summer precipitation anomalies over China. The results demonstrate that IAP-DCPv3.5 exhibits reasonable predictive capability for summer precipitation anomalies in eastern China, with the bias correction method significantly improve the system"s prediction skills for summer precipitation. Comparation of prediction skills with different lead-time suggests that, the prediction skill for summer precipitation initialized in March is generally comparable to that initialized in May. Evaluation of prediction skills for summer monsoon circulation shows that IAP-DCPv3.5 can successfully predict the interannual variation of East Asian Summer Monsoon Index and reasonably reproduce the relationship between monsoon activity and precipitation anomalies in eastern China, indicating that the East Asian summer monsoon prediction skill can significantly affect the predictive ability of summer precipitation in China. Real-time prediction and validation for 2023 summer precipitation further demonstrate that IAP-DCPv3.5’s capability in predicting the observed spatial distribution of summer precipitation anomalies over China, despite discrepancies in the amplitude of predicted rainfall anomalies can be found. This study implies that IAP-DCPv3.5 could be directly applied for the real-time flood season prediction in China, and the prediction validation based on this prediction system can also provide important scientific basis for the further improvement of CAS-ESM2.0.