Precise weather monitoring and accurate weather forecast are two of the most decisive factors for the success of the Winter Olympics.Considering the particularity of the 2022 Beijing Winter Olympic Games (the only Winter Olympic Games held under the climate dominated by the continental East Asian winter monsoon and the only Winter Olympic Games held in inland areas) and the rigid demand for the goal of “hundred-meter resolution and minute-updated level” high-precision forecast,the Beijing Institute of Urban Meteorology has developed a new generation of the Rapid-refresh Integrated Seamless Ensemble system—RISE—that can provide 500-and even 100-m resolution spatial grid forecast data products with 10-min updated frequency for the Beijing Winter Olympics.In order to improve the prediction accuracy of the RISE system,and considering the successful use of deep learning in the field of geoscience in recent years,this paper develops a convolution neural network-based model,Rise-Unet,using the high-resolution RISE data from 2019 to 2021 to correct the prediction results of 2-m surface temperature,2 m-relative humidity,10-m U wind speed,and 10-m V wind speed for a lead time of 4—12 hours.The root-mean-square error and mean absolute error are employed to evaluate the accuracy of the model in this study.By comparing with the original prediction results of the RISE system,it is proven that the deep learning-based model,Rise-Unet,can effectively improve the accuracy of high-resolution gridded prediction results.The method proposed in this study can be applied as the post-processing module of the RISE system,which has important scientific significance and application value for improving the grided prediction level of the RISE system as well as other high-resolution numerical weather forecasting systems.