The identification of precipitation regimes is important for many purposes such as agricultural planning, water resource management, and return period estimation. Since precipitation and other related meteorological data typically exhibit spatial dependency and different characteristics at different time scales, clustering such data presents unique challenges. In this short paper, we develop a flexible model-based approach to identify precipitation regimes in China by clustering spatial functional data. Though the focus of this study is on precipitation data, this methodology is generally applicable to other environmental data with similar structure.