Rainy images degrade the visional performance that may bring down the accuracy of various applications. In this paper, we propose a novel densely connected network with Dense Feature Pyramid Grids Modules, called DFPGN, to solve the rain removal task. Specifically, in the proposed DFPG, there are five operations from different layers with various pathways and scales as the input of the current layer so that each layer can fuse various features from shallower and deeper ones to improve the deraining ability of the net- work. Extensive experiments on real and synthetic rainy images are conducted to demonstrate the proposed method achieves superior rain removal performance over state-of-the- art approaches.