Data augmentation is a key technique to improve the generalizability of change detection models. Although the existing data augmentation methods achieve promising performance in image classification and object detection, they ignore the differences among the time-series images and the diversities of the changed objects. In order to preserve the change region and increase the information of the complex background, this paper proposed a novel data augmentation method for change detection based on change region mask, called MaskMix. Firstly, the change regions of the current image pair were pasted on an image pair to generate a new change image pair having new background and new changes. Secondly, MaskMix further augmented the image pair by multi-path weighted fusion strategy. It selected a classical image processing method randomly from an image processing set for each path to conduct further augmentation. And then the processed image pairs from K paths were fused using a K-dimensional weight generated by Dirichlet distribution. Finally, the pre-processed image pair and the post-processed image pair were fused by the weight generated by the Beta distribution through the skip connection. Experiments conducted on two publicly available datasets, e. g., BCD(build change detection) and LEVIR-CD(LEVIR building change detection dataset), demonstrate that MaskMix significantly improves the generalizability of change detectors, e. g., ADCDNet, BIT, ChangeFormer, SNUNet, and DSAMNet. Moreover, compared with the existing image augmentation methods, such as MixUp, AugMix, MUM, and CropMix, MaskMix effectively increases the complexity and diversity of change images, enhancing the generalizability of existing change detection methods.