The frequent alternation of cold and warm events in the winter months has increased the difficulty and challenge of short-term climate prediction. Additionally, the overall prediction level of the climate dynamic models for winter temperatures in Ningxia was not high, resulting in an unstable prediction quality. The development of the model interpretation application method, combining dynamics and statistics, was effective in improving the prediction quality and is crucial for the urgent development of the provincial short-term climate prediction business. This article is based on the EC model historical calculations over the past 30 years of the MODES second-generation products of the National Climate Center, the monthly average winter temperatures observation data from 19 national meteorological stations in Ningxia, and the NCEP/NCAR atmospheric reanalysis data. Using the similarity error correction method, we combined the information of key circulation areas during the same period for model interpretation and application of winter temperatures to improve the accuracy and objectivity of climate trend prediction in Ningxia. The results revealed that the original prediction outcomes of the EC model have relatively high prediction skills for winter temperatures, especially regarding grasping trends and abnormal levels. After adopting a similar error correction scheme, the EC model can still effectively improve its prediction skills for winter temperatures in Ningxia, with a particularly significant improvement in December and January. After correction, the PS and PC scores were higher than 70% and 64%, respectively. Additionally, when the average temperature anomaly was positive in January and negative in December and February, the prediction skills improved more significantly; the larger the magnitude of the lower temperature, the more significant the improvement. Moreover, the magnitude of the model error did not significantly impact the forecast correction effect. Even when the absolute value of the model error was large, this correction scheme could still improve the winter monthly temperature prediction skills to varying degrees. Therefore, the similarity error correction method could further improve the forecast accuracy of the winter temperature trend and anomaly level in Ningxia under large model errors, improving the stability of the model forecast skill and providing a positive application value in practical service.