Single image rain streak removal is extremely necessary since rainy images can seriously affect many computer vision systems. In this paper, we propose a novel recurrent context-aware multi-stage network (ReCMN) for image rain removal that gradually predicts clean derained results. Specifically, the ReCMN introduces a multi-stage strategy to perform contextual relationship modeling. Firstly, we use the densely residual extraction block (DREB) to guide feature extraction. Then, a multi-scale context aggregation block (MCAB) is designed to utilize the long-distance dependencies and multiple scale features, which can fuse features of different levels to fully exploit contextual information. Finally, we develop a parallel attention block (PAB) to capture the channel and spatial information and only pass effective feature representation. Experimental results demonstrate that our method outperforms several state-of-the-art methods, based on both synthetic datasets and real-world rainy images.