Satellite-based precipitation estimations provide frequent, large-scale measurements. Deep learning has recently shown significant potential for improving estimation accuracy. Most studies have employed a two-stage framework, which is a sequential architecture of a rain/no-rain binary classification task followed by a rain rate regression task. This study proposes a novel precipitation retrieval framework in which these two tasks are simultaneously trained using multi-task learning approach (MTL). Furthermore, a novel network architecture and loss function were designed to reap the benefits of MTL. The proposed two-task model successfully achieved a better performance than the conventional single-task model possibly due to efficient knowledge transfer between tasks. Furthermore, the product intercomparison showed that our product outperformed existing products in rain rate retrieval and also yielded better skills in the rain/no-rain retrieval task.