The previous methods for precipitation nowcasting based on deep learning try to model the spatiotemporal evolution of radar echoes in a unified architecture. However, these methods may face difficulty in capturing the complex spatiotemporal relationships completely. This study proposes a two-stage precipitation nowcasting network based on the Halo attention mechanism. This network divides the spatiotemporal evolution process of precipitation nowcasting into two stages: motion trend prediction and spatial appearance reconstruction. Firstly, a learnable optical flow module models the motion trend of radar echoes and generates coarse prediction results. Secondly, a feature reconstruction module models the spatial appearance changes in the historical radar echo sequences and refines the spatial appearance of the coarse-grained prediction results, generating fine-grained radar echo maps. The experimental results on the CIKM dataset demonstrate that the proposed method outperforms mainstream methods. The average Heidke skill score and critical success index are improved by 4.60% and 3.63%, reaching 0.48 and 0.45, respectively. The structural similarity index is improved by 4.84%, reaching 0.52, and the mean squared error is reduced by 6.13%, reaching 70.23.