Two ensemble experiments with an atmospheric general circulation climate model (IAP9L_CoLM) were conducted to investigate the impacts of realistic Eurasian snow condition on spring climate predictability over China. In one ensemble simulation, snow condition was allowed to evolve interactively; in the other ensemble simulation, the snow condition was prescribed according to microwave remote snow water equivalent depth data. Differences between the two experiments were assessed to evaluate the impact of realistic Eurasian snow condition on simulated climate anomalies in the spring. Looking at sea level pressure and geopotential height at the middle and upper levels, it was found that the IAP9L_CoLM prediction skill was enhanced at the middle-high latitudes of Eurasia with improved Eurasian snow condition. Furthermore, the results indicate that the predictive skill of IAP9L_CoLM for both interannual variation and spatial distribution of surface air temperature over China was obviously improved with prescribed snow condition over Eurasia. For spring precipitation over China, although the predictive skill was low, the potential predictability increased with more realistic snow condition. One case study also showed that with the improved simulation of snow condition over Eurasia, the predictive skill for large scale circulation anomalies over the middle-high latitudes of Eurasia was increased, which finally led to a better hindcast of spring climate anomalies over China. Overall, this study suggests that Eurasian snow condition can potentially affect the spring climate anomalies over China. Thereby the predictive ability for Eurasian snow condition must be improved for better prediction of spring climate over China.