Abstract: Accurate satellite-based precipitation estimation is important for research on weather, climate, hydrology and ecology, and can reduce the loss caused by natural disasters such as floods that are directly caused by precipitation. At present, operational satellite-based precipitation products are mainly obtained by various physical retrieval algorithms, which may miss certain hidden precipitation-related features. In recent years, with the development of deep learning, its ability to mine hidden features has been gradually introduced into the research of various nonlinear processes. In this paper, we propose a deep learning model named Attention-Unet for satellite-based precipitation estimation. The high spatiotemporal resolution spectral data of the Fengyun-4A satellite (FY-4A) are used to train the model. To evaluate the effectiveness of the proposed model, we compare model outputs with operational satellite-based precipitation products and products of other mature deep-learning models. Statistics and visualizations of the experimental results show that the proposed model performs better than the operational satellite-based precipitation retrieval algorithms and other mature deep learning models in both precipitation identification and precipitation amounts estimation. The experimental results show that the model has the potential to be applied to meteorological work. Furthermore, based on FY-4A multi-channel data, we add topography data to the training datasets. The experimental results show that after adding topography data to training datasets, the model performs better than before in the estimation of precipitation amount, especially in the mountainous region. The above results shows that Attention-Unet has a great application potential and prospect in satellite precipitation estimation.