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In the process of remote sensing data collection, due to sensor failures, meteorological conditions, and other reasons, a small number of abnormal points may appear in the collected data, which may have a negative impact on the accuracy of extreme weather forecasting. Therefore, it is necessary to study a method for detecting outliers in extreme weather forecast data based on satellite remote sensing monitoring. Based on the improved K-means clustering algorithm, the missing satellite remote sensing monitoring extreme weather forecast data is interpolated to restore data integrity. Divide extreme weather forecast data sections for satellite remote sensing monitoring, extract four feature parameters for each section, and use them as inputs to optimize BP neural network recognition of abnormal sections using bat algorithm. Calculate the local outlier factor of extreme weather forecast data monitored by each satellite remote sensing in the abnormal section. Data with a local outlier factor greater than 1.0 are considered abnormal values of weather forecast data, in order to complete the detection of abnormal values of weather forecast data. The results show that the interpolation error of the proposed method is less than ± 1.0, which can accurately identify outliers in the abnormal section. Moreover, the coordination index in different samples is higher than 0.8, and the detection effect is better.