This study aims to enhance the accuracy of urban grid temperature forecasts by integrating multi-scale features from both upper-air and surface levels. Utilizing forecast data from the European Centre for MediumRange Weather Forecasts（ECMWF）model and hourly temperature grid observation data from the China Meteorological Administration's High-Resolution Land Data Assimilation System（HRCLDAS）for the Changde region from April to September 2021-2024，a high-resolution Multi-Scale U-Net（MU-NET）model was employed. Three sets of experiments were designed to develop a deep learning model capable of predicting hourly temperatures in Changde City for the next 24 hours. The experimental results demonstrate that the MU-NET model， which integrates surface and upper-air features，exhibits the best correction performance within the study area. The mean absolute error（MAE）and root mean square error（RMSE）of the MU-NET model were reduced by 22% and 25%，respectively，compared to the ECMWF model. Additionally，the MU-NET model achieved the lowest MAE values in diurnal variations，particularly excelling in the prediction of daily maximum temperatures，with an average reduction of over 0. 4 ℃. On a spatial scale，the MU-NET model showed a 60%~80% improvement in forecast skill scores over the ECMWF model in high-altitude regions，with the most significant improvements observed in plain and Dongting Lake areas. During two critical weather events in 2024，the MUNET model demonstrated stable forecast performance due to its integration of surface and upper-air features，particularly in handling complex weather phenomena. The findings of this study provide new insights for improving the accuracy of temperature forecasts and offer valuable references for practical meteorological forecasting.