With the application of deep learning (DL) in signal detection, improving the robustness of classification models has received much attention, especially in automatic modulation classification (AMC) of electromagnetic signals. To obtain robust models, a large amount of electromagnetic signal data is required in the training and testing process. However, both the high cost of manual collection and the low quality of data samples from automatically generated data result in the defects of the AMC models. Therefore, it is important to generate electromagnetic data by data augmentation. In this paper, we propose a novel electromagnetic data augmentation tool, namely ElecDaug, which directs the metamorphic process by electromagnetic signal characteristics to achieve automatic data augmentation. Based on electromagnetic data pre-processing, transmission or time-frequency domains characteristic metamorphic, ElecDaug can augment the data samples to build robust AMC models. Preliminary experiments show that ElecDaugcan effectively augment available data samples for model repair. The video is at https://youtu.be/tqC0z5Sg1_k. Documentation and source code can be found here: https://github.com/ehhhhjw/tool_ElecDaug.git.