The application of AI and machine learning techniques to meteorological data has significantly enhanced the accuracy and response speed of extreme weather warnings, as well as the analysis of climate trends. However, the existing climate models suffer from constraints imposed by computational resources and model complexity, leading to outputs with coarse spatio-temporal resolution. Current meteorological super-resolution techniques predominantly focus on single-dimensional (spatial or temporal) enhancements, failing to effectively reconstruct dynamic spatio-temporal coupled features. To address these limitations, this study proposes a Spatio-Temporal Multi-Scale Residual Network (ST-MSRN), which integrates a Multi-Scale Residual Feature Block (MSRFB) with a Channel Stacking Mechanism. The framework employs parallel multi-scale convolutions to hierarchically extract meteorological patterns, while the integrated Efficient Multi-scale Attention (EMA) module adaptively weights features based on spatio-temporal heterogeneity. Experimental results demonstrate: (1) Successful upscaling from 1.5° spatial/3-day temporal to 0.25°/daily resolution; (2) Superior performance over traditional methods (spline/nearest-neighbor interpolation) and mainstream deep learning methods, with marked improvements in key indicators such as structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) for temperature and precipitation data, while the mean absolute error (MAE) and mean squared error (MSE) have been significantly reduced. This work establishes a new paradigm for Earth system data enhancement, particularly advancing extreme weather early warning systems through physics-aware deep learning architectures.