A real-time abnormity-diagnosing and interpolation method for water regime-monitoring data relates to the technical field of monitoring water regime. This method includes the following steps: acquiring water regime-monitoring data, drawing a box plot, recognizing and diagnosing abnormal data in real time based on the box plot, performing grey correlation analysis on other variables related to a predictor variable, building a BP neural network model and making training, applying the BP neural network model to predict water regime-monitoring data in real time, and performing abnormity diagnosis and data interpolation. Adopting this method, we can effectively enhance predicting and monitoring the water regime-monitoring data in real time, and diagnose abnormal data and make interpolations in time, thereby improving the reliability of data, objectively reflecting water regime changes, and effectively guiding engineering scheduling.