As a fundamental and challenging problem in computer vision, hand pose estimation aims to estimate the hand joint locations from depth images. Typically, the problems are modeled as learning a mapping function from images to hand joint coordinates in a data-driven manner. In this paper, we propose a context-aware deep spatiotemporal network, a novel method to jointly model the spatiotemporal properties for hand pose estimation. Our proposed network is able to learn the representations of the spatial information and the temporal structure from the image sequences. Moreover, by adopting the adaptive fusion method, the model is capable of dynamically weighting different predictions to lay emphasis on sufficient context. Our method is examined on two common benchmarks, the experimental results demonstrate that our proposed approach achieves the best or the second-best performance with the state-of-the-art methods and runs in 60 fps.