Abstract: Numerical weather prediction (NWP) plays a central role in meteorological forecasting, and enhancing spatial resolution using deep learning techniques is a key direction in spatial downscaling. However, traditional generative models often suffer from low computational efficiency, insufficient spatial texture accuracy, and mode collapse. To address these issues, this paper proposed a spatial downscaling method for NWP based on a residual shift diffusion model. Specifically, we introduced residuals into the model' s diffusion process, aiding in quicker convergence and reducing the number of sampling steps. Unlike latent space diffusion models, our approach decreases inference time by a factor of 40, thereby significantly enhancing computational efficiency. The step-by-step denoising process of the diffusion model generated high-quality, detail-rich downscaled images, thereby enhancing the representation of spatial details. Furthermore, we designed a flexible noise control mechanism that effectively mitigated mode collapse, thereby ensuring the diversity of the generated images. In comparative experiments with generative adversarial models, our method significantly improved peak signal-to-noise ratio, structural similarity, and learned perceptual image patch similarity by 6%, 12%, and 16%, respectively. This approach promises to provide more accurate and detailed weather information for weather-sensitive industries, driving technological advancements in related fields.