Precipitation nowcasting uses previous rainfall observations to forecast future rainfall intensities in a local area. In rainfall data, the rain-less samples usually well exceed the heavy rainfall samples, and it causes the data imbalance problem in precipitation nowcasting tasks. In this paper, we find that if the imbalance ratio is fixed, tasks with higher mutual information make the nowcasting model more robust to the data imbalance problem. Based on this observation, we propose a mutual information-based reweighting strategy. The reweighting strategy allows the neural network models to achieve better performance on minorities without compromising the performance of majorities and overall nowcasting image quality. Extensive experimental results demonstrate that this proposed approach is effective and compatible with state-of-the-art models.