Beyond high accuracy, good interpretability is very critical to deploy a face forgery detection model for visual content analysis. In this paper, we propose learning patch-channel correspondence to facilitate interpretable face forgery detection. Patch-channel correspondence aims to transform the latent features of a facial image into multi-channel interpretable features where each channel mainly encoders a corresponding facial patch. Towards this end, our approach embeds a feature reorganization layer into a deep neural network and simultaneously optimizes classification task and correspondence task via alternate optimization. The correspondence task accepts multiple zero-padding facial patch images and represents them into channel-aware interpretable representations. The task is solved by step-wisely learning channel-wise decorrelation and patch-channel alignment. Channel-wise decorrelation decouples latent features for class-specific discriminative channels to reduce feature complexity and channel correlation, while patch-channel alignment then models the pairwise correspondence between feature channels and facial patches. In this way, the learned model can automatically discover corresponding salient features associated to potential forgery regions during inference, providing discriminative localization of visualized evidences for face forgery detection while maintaining high detection accuracy. Extensive experiments on popular benchmarks clearly demonstrate the effectiveness of the proposed approach in interpreting face forgery detection without sacrificing accuracy. The source code is available at https://github.com/Jae35/IFFD.