Temperature is a core element of the regional climate system and plays a key role in energy exchange and weather evolution. The current reanalysis of temperature data faces difficulties in providing more accurate geographical temperature data due to insufficient spatial resolution (0.25° × 0.25°). In this study, a lightweight downscaling method incorporating a convolutional neural network is proposed to construct a high-resolution temperature prediction model for the Macau region based on ERA5 reanalysis data. Aiming at the existing data due to insufficient resolution, a two-stage convolutional feature extraction module is introduced to optimize the model parameters by combining them with the observation data of Macau meteorological stations. The experimental results show that the accuracy of this method is 21.4% higher than that of the traditional interpolation method in the instantaneous prediction, and the prediction effect in the next 3 h is also very good. The model is expected to be extended to other regions in the future, providing an effective solution for obtaining long-term high-resolution temperature data in other regions, which can support the refinement of meteorological services and climate research.