A novel single-image rain removal method is proposed based on multi-scale cascading image generation (MSCG). In particular, the proposed method consists of an encoder extracting multi-scale features from images and a decoder generating de-rained images with a cascading mechanism. The encoder ensembles the convolution neural networks using the kernels with different sizes, and integrates their outputs across different scales. The decoder implements a coarse-to-fine image generation framework, adding fine details incrementally to the final de-rained images according to the spatial contextual information on different scales. We test the proposed method on both synthetic and real-world datasets. Experimental results show that the proposed method is robust to the changes of scene, e.g., the viewpoint and the depth, the heaviness of rain, etc., which suppresses the blurring problem of de-rained image and outperforms state-of-the-art methods consistently.