Deep learning based shadow removal aims to learn a mapping that translates shadow image from shadow domain to non-shadow domain. In this work, we develop a sim-ple yet effective target-consistency generative adversarial net-work (TC-ShadowGAN) for the task of unpaired shadow re-moval without any additional label for supervision. Com-pared with the popular bidirectional mapping based methods, TC-ShadowGAN targets to directly learn a one-side mapping to translate shadow images into shadow-free ones. With the proposed target-consistency constraint that is designed to connect a couple of GAN-based sub-networks, the corre-lations between shadow image and the output shadow-free image, and the realness of recovered shadow-free image are strictly confined. Extensive quantitative and qualitative evalu-ation experiments results show that TC-ShadowGAN outper-forms the state-of-the-art unpaired shadow removal methods by 14.9% in terms of FID and 31.5% in terms of KID on the unpaired shadowremoval dataset. The code isavailable at https://github.com/chaotan/TC-GAN.