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In order to improve the availability and accuracy of online monitoring data of water resources, it is very important to detect and correct the outliers of monitoring data. The water resources monitoring data are non-linear and non-stationary time series data, the outlier detection method of the conventional time series did not take into account the convexity and concavity of time series. A combining median and ensemble empirical mode decomposition (EEMD) method was presented for outlier detection. Firstly, the outliers were preliminarily detected by the median method. And then the remaining data were decomposed by EEMD. The overall trend of most of the data can be fitted by superimposing the low-frequency components, but not affected by outlier, and the outlier can be detected effectively according to the deviation rate. Then, according to change of convexity and concavity of time series data after outlier detection, the method of piecewise curve fitting was used to correct the outliers. Finally, taking the daily water intake data of H1 waterworks as an example, the results showed that the method of combining median and EEMD can detect outliers effectively. The data obtained after correction can truly reflect the actual situation of water intake of waterworks. It can also provide more reliable data for subsequent analysis.