In the past decade, rapid advancements in correction techniques based on numerical forecasts have yielded significant improvements in meteorological element (especially temperature) predictions. However, in operational applications, the absolute accuracy of forecasts in complex terrain regions remains relatively low, with notable forecast errors persisting in transitional weather conditions such as cloudy weather. Building upon this foundation, this study focuses on Model Output Statistics (MOS) forecast corrections. Prior to correction, employing two technical methods—spatial interpolation optimization (using Cressman interpolation and locally optimizing the interpolation radius from 0.2° to 1.4°) and cloud cover grouping optimization (based on the numerical model’s total cloud amount (TCC) and low cloud amount (LCC), with a cross-grouping of 45 groups ranging from 0.9 to 0.1 for local optimization)—further mitigates the impact of terrain on temperature forecasts and enhances forecast accuracy in transitional weather conditions. Upon examination, the MOS forecast results for temperature have achieved an additional 3–4% improvement based on the adoption of these two strategies. This enhancement holds significant importance in forecasting operations. The meticulous application of data processing and optimization techniques on temperature corrections has not only enhanced forecast accuracy but also strengthened the forecasting capabilities for specific regions or weather conditions.