Accurate estimation of solar radiation is of great significance for solar energy development and climate research. However, in China, the scarcity and uneven distribution of observation stations often cause deep learning models to overfit and suffer from accuracy degradation under small-sample conditions. To address this issue, this paper proposes a deep learning framework that integrates transfer learning and multi-scale time series modeling for predicting hourly global solar radiation at target meteorological sites. The method employs representation learning and clustering to select source domain sites with similar climatic characteristics. It integrates wavelet transform convolution, depthwise separable convolution, and a Transformer encoder–decoder to achieve multi-scale feature extraction and long-term dependency modeling. Experimental results demonstrate that the model achieved a coefficient of determination (R²) of 0.9710 in tests conducted in the Ningxia region. It maintained good predictive performance even in a cold-start scenario with only one month of training data and exhibited stable accuracy across all four seasons, effectively mitigating seasonal bias. This provides a reliable solution for solar radiation estimation in data-scarce regions, and its modeling approach can also be extended to other climate-related time series prediction tasks.