Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerged to tackle a more challenging, yet realistic setting, where only one source domain is available at training time. The existing Single-DG approaches typically are based on data augmentation strategies and aim to expand the span of source data by augmenting out-ofdomain samples. Generally speaking, they aim to generate hard examples to confuse the classifier. While this may make the classifier robust to small perturbation, the generated samples are typically not diverse enough to mimic a large domain shift, resulting in sub-optimal generalization performance. To alleviate this, we propose a centeraware adversarial augmentation technique that expands the source distribution by altering the source samples so as to push them away from the class centers via a novel angular center loss. We conduct extensive experiments to demonstrate the effectiveness of our approach on several benchmark datasets for Single-DG and show that our method outperforms the state-of-the-art in most cases.