RF fingerprinting involves identifying characteristic transmitter-imposed variations within a wireless signal. Deep neural networks (DNNs) that do not rely on handcrafting features have proven to be remarkably effective in fingerprinting tasks, as long as the channel remains invariant. However, DNNs trained at a specific location and time perform poorly on datasets collected under different channel conditions. This article proposes a data augmentation step within the training pipeline that exposes the DNN to many simulated channel and noise variations that are not present in the original dataset. We describe two approaches for data augmentation. The first approach is applied to the "transmitter data" when transmitter side data (i.e., pure signals without channel distortion) is available. The second approach is applied to the "receiver data" when only a passive dataset is available with already over-the-air transmitted signals. We show that data augmentation results in 75 percent improvement in the former case with a custom-generated dataset, and around 32-51 percent improvement in the latter case on a 5000-device WiFi dataset, compared to the case of non-augmented data fed to DNNs.