Compared to the legacy GOES Imager, the GOES-16 Advanced Baseline Imager (ABI) provides measurements at much higher resolution in both spatial and temporal dimensions, which benefits many applications such as rainfall rate estimation. However, it is challenging to quantify the water droplets in the cloud and precipitation intensity with only the Infrared (IR) radiation and converted brightness temperature information. Comparison between ground radar rainfall estimates and the operational GOES-16 rain rate products indicates that uncertainty associated with GOES-16 rain rate estimates is significant. This paper proposes an innovative deep learning system for precipitation estimation using measurements from GOES-16/ ABI. Cloud-top brightness temperature information observed by channels 8/10/11/14/15 of the ABI are used as input to this deep learning framework. The rainfall estimates from a ground radar network are used as target labels in the training phase. Independent verification shows that the deep learning-based rainfall system can estimate precipitation time, intensity, and amount very well, especially in heavy rain regions.