To address radar coverage blind spots in complex terrain, this study proposes an end-to-end deep learning framework to retrieve Radar Composite Reflectivity (RCRF) from FY-4A satellite multi-channel observations. We introduce CBAM-UNet, embedding a lightweight Convolutional Block Attention Module into a U-Net backbone. This dual-dimensional mechanism adaptively filters critical infrared spectral bands and precisely localizes intense convective cores. Evaluated on a comprehensively matched satellite-radar dataset (14,023 samples) from Sichuan Province (May–November 2023), CBAM-U-Net significantly outperforms mainstream CNN and Transformer baselines in retrieval accuracy (RMSE = 6.8290 dBZ, <em>R</em><sup>2</sup> = 0.6277) and structural fidelity (SSIM = 0.7894). Crucially, within the challenging severe echo regime (45–70 dBZ), the model achieves optimal Probability of Detection (POD = 0.5296) and Critical Success Index (CSI = 0.4384). Furthermore, crosssensor evaluations using FY-4B data demonstrate its robust zero-shot generalization against observational domain shifts. This research highlights the efficacy of integrating satellite multispectral features with attention-augmented networks to compensate for radar blind spots, providing reliable support for severe convective weather monitoring.