It is well-known that real-world changes constituting distribution shift adversely affect model performance. How to characterize those changes in an interpretable manner is poorly understood. Existing techniques take the form of shift explana- tions that elucidate how samples map from the original distribution toward the shifted one by reducing the disparity between the two distributions. However, these methods can introduce group irregularities, leading to explanations that are less feasible and robust. To address these issues, we propose Group-aware Shift Explanations (GSE), an explanation method that leverages worst-group optimization to rectify group irregularities. We demonstrate that GSE not only maintains group structures, but can improve feasibility and robustness over a variety of domains by up to 20% and 25% respectively.