Adaptive image steganography has been becoming a hot topic, as it conceals covert information within the texture region of an image by employing a defined distortion function, which guarantees remarkable security. In spatial gray-scale image steganography, the research on automatically generating steganographic distortion using the generative adversarial network has achieved a significant breakthrough recently. However, to the best of our knowledge, there are not related works in spatial color image steganography. Compared with the gray-scale image, color image steganography should preserve the channel correlation and reasonably assign the embedding capacity among RGB channels simultaneously. This paper first proposes a framework based on generative adversarial network to automatically learn to generate the steganographic distortion for spatial color image, which is termed as CIS-GAN (color image steganography based on generative adversarial network). The generator is composed of two U-Net subnetworks, one of two subnetworks translates a cover image into a modification probability map which is the sum of positive/negative modification probability, while another one learns the proportion of positive modification probability. The structure of the designed generator can effectively preserve RGB channels correlation, so as to enhance the steganography security. Also, the generator can automatically learn to allocate the embedding capacity for three channels via controlling the total steganographic capacity in generator's loss function and alternately training the discriminator. The experimental results show that our proposed framework outperforms the advanced spatial color image steganographic schemes in resisting the color image steganalysis.