Geostationary satellites (GEOs) provide real-time information on the atmosphere and can be used for quantitative precipitation estimation (QPE) with low latency. The relation between GEO observations and precipitation is complex, though, limiting QPE performance.</p> <p>Here we present a conditional generative adversarial network (cGAN) to perform QPE using data from SEVIRI infrared and water vapor channels. The model is trained with precipitation maps derived from the German radar network (RADKLIM-YW).</p> <p>The model is evaluated in Germany for a separate time period and compared with the operational GEO QPE product PDIR-Now. Our main findings are: 1. The cGAN produces more realistic precipitation structures and intensities (including high precipitation rates) and has a higher location accuracy then the benchmark product; 2. Ensemble predictions are spatially under dispersed, uncertainty in precipitation intensity is captured better than uncertainty in precipitation location. Ensemble spread and other limitations, such as spurious amplified errors at extreme intensities, do not substantially reduce the overall skill improvements over the benchmark product. Future work should focus on extending the framework across a other geographic domains to assess generalization under varying viewing geometries and precipitation regimes.