URHand is the first universal light-aware hand model that can generalize across different viewpoints, poses, lighting conditions, and identities. The model can be personalized with a few shots from a mobile phone and render realistically under new lighting conditions. Based on neural network multi-view hand images and light, we build a strong universal light prior. We propose a neural renderer that utilizes a spatially-varying linear illumination model and physically-motivated shadows as input features. By removing non-linear activations and biases, our specifically designed illumination model explicitly maintains the linearity of light transport. We also introduce joint learning of physically-based models and neural light models, further enhancing the fidelity and generalization performance. Extensive experiments demonstrate that our method outperforms existing methods in both quality and generalization ability. We also demonstrate how to achieve rapid personalization for unseen identities from mobile phone shots.