Abstract: Reference crop evapotranspiration (ET0) is one of the most important hydroclimatic variables for the hydrologic and crop models, as well as the actual evapotranspiration in the irrigation schedule. The ET0 prediction a few months in advance can be beneficial to the decision-making on the long-term planning in the water management and irrigation communities. In this research, the monthly predictions of ET0 were realized over the Huaihe River basin using the model data of Beijing Climate Center Second-Generation Climate Prediction System (BCC_CPSv2), and the surface meteorological observations at 172 stations during 1991 to 2020. The bilinear interpolation and quantile mapping were also selected to downscale and then correct the mean air temperature, net radiation, relative humidity, and wind speed of BCC_CPSv2. The obtained four variables were used to calculate the ET0 predictions using the Penman-Monteith equation. The correlation coefficient (r), Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were employed to evaluate the ET0 prediction performances of the model, and the four climatic variables before and after correction. Results showed that the mean air temperature, net radiation, and relative humidity from the model were significantly smaller than those from the observations before correction in each month with the RMSE of 1.84℃, 1.70 MJ/(m2·d), and 15.79%, respectively. The wind speed was also smaller during March to June, but larger in other months, with the RMSE of 1.39 m/s. Errors in the climatic variables led to the lower ET0 during February to June, and the larger ET0 in January, as well as in July to December, compared with the calculations. The RMSE of ET0 before correction was ranged from 0.11 to 1.70 mm/d for each month, among which was the highest in summer. The larger RMSE exceeding 0.50 mm/d occurred in eastern coastal areas in January and October, while the southwestern mountainous areas presented the higher RMSE exceeding 0.70 mm/d in April and July. The model skills on predicting climatic variables and ET0 were improved effectively by the quantile mapping. The RMSE of air temperature, net radiation, relative humidity, and wind speed decreased to 1.32℃, 0.74 MJ/(m2·d), 7.38%, and 0.53 m/s, respectively. Especially, the MAPE of wind speed and net radiation was decreased by more than 19%. The RMSE of ET0 after correction was ranged between 0.05 and 1.22 mm/d for each month, indicating the decrease in 80% of the months. The performances of the model were improved significantly for the eastern coastal areas in January, and the southwest mountainous areas in April, July and October, where the RMSE decreased by more than 0.30 mm/d. Before and after the correction, the net radiation and relative humidity were the primary factors for the ET0 prediction errors in summer half year and winter half year, respectively, due to the larger errors of the model and the higher sensitivity of ET0 to them. A better performance was achieved in the monthly ET0 prediction after the model correction with the error about 10.7%. The finding can provide a strong reference for the water resources management, irrigation schedule planning, and agricultural water demand decision-making.