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In this paper, we present a spatiotemporal deep learning (STDL) network to conduct binary phase scintillation forecasting at a high-latitude global navigation satellite systems (GNSS) station. Historical measurements from the target and surrounding GNSS stations are utilized. In addition, external features such as solar wind parameters and geomagnetic activity indices are also included. The results show that the STDL network can adaptively incorporate spatiotemporal and external information to achieve the best performance by outperforming a naive method, three conventional machine learning algorithms (logistic regression, gradient boosting decision tree, and fully connected neural network) and a machine learning algorithm known as long short-term memory that incorporates temporal information.