In this paper, we propose a context-wise attention-guided network for single image deraining. Unlike most existing deraining methods, our network exploits underlying complementary information not only across multiple scales but also between levels. Specifically, our network architecture is designed to transmit the inter-level and inter-scale features. To extract guiding information and improve the discriminating ability of context-wise attention-guided network, we propose a net-context-wise attention module to generate attention maps. Following residual learning, the clean image is created by removing the predicted rain streak layer from the rainy input. Experimental results show our method has better performance on public datasets than some state-of-the-art methods.