冬季月和季节内频繁交替的冷暖事件增大了短期气候预测的难度和挑战性，加之气候动力模式对于宁夏冬季气温的预测水平整体不高，导致预测质量不稳定。动力与统计相结合的模式解释应用方法的发展，为预测质量的提升提供了有效的技术手段，也是省级短期气候预测业务亟须发展的重要方向。基于国家气候中心MODES二代产品的EC模式近30 a历史回算数据、宁夏19个国家气象站冬季逐月平均气温观测数据、NCEP/NCAR大气再分析资料等，采用相似误差订正方法，利用同期环流关键区信息对宁夏冬季月气温开展模式解释应用，旨在提高宁夏气候趋势预测准确率和客观化水平。结果表明：EC模式原始预测结果对宁夏冬季各月气温的预测技巧整体较高，尤其对于趋势和异常量级的把握能力较好；采用相似误差订正方案后，仍能有效提高EC模式对宁夏冬季气温的预测技巧，其中12月和1月预测技巧提高尤为明显，订正后PS、PC评分分别高于70%和64%。当1月平均气温为正距平、12月和2月为负距平时预测技巧提高更明显，气温偏低幅度越大提高越显著；模式误差大小对预报订正的效果无明显影响，即使在模式误差绝对值较大情况下，该订正方案仍能不同程度地提升冬季各月模式气温预测技巧。因此，相似误差订正方法可以在模式误差较大的情况下，进一步提高宁夏冬季气温趋势和异常量级的预报准确性，改进模式预报技巧的稳定性，在实际业务中具有良好的应用价值。

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.