Meteorological satellite remote sensing offers a crucial solution to the issue of insufficient ground-based observation coverage in regions with complex terrains. However, being an indirect method for measuring meteorological elements, current satellite-based precipitation estimation still suffers from limited accuracy. This paper proposes a novel Dual-Stream Transformer-Augmented UNet (DSTA-UNet) framework for satellite quantitative precipitation estimation (QPE). It generates high-accuracy precipitation analysis fields in near real-time by using the satellite cloud imagery from China’s new-generation geostationary satellite, Fengyun-4A (FY-4A). Extensive comparisons against operational products [FY-4A QPE, Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG), and Climate Forecast System Reanalysis Version 2 (CFSv2)] and deep-learning-based (Attention-UNet and UCTransNet) precipitation estimation methods demonstrate that DSTA-UNet achieves competitive performance. When evaluated against ERA5 as the benchmark, DSTA-UNet reduces the root mean square error (RMSE) by 52.38% for FY-4A QPE, 52.27% for GPM IMERG, 27.02% for CFSv2, 33.65% for Attention-UNet, and 0.71% for UCTransNet. Using station observations as the benchmark, DSTA-UNet also achieves significant RMSE reductions: 25.80% for FY-4A QPE, 14.23% for GPM IMERG, 8.51% for CFSv2, and 3.72% for ERA5.