Abstract: Streamflow similarity regionalization is significant for interpolating and transferring streamflow data and regional flood frequency analysis. To accurately identify the similarities and differences of streamflow characteristics of different hydrological stations in a hydrometric network and improve the accuracy of streamflow similarity regionalization results, we introduce the copula entropy method to estimate the mutual-information-based R statistics, aiming at measuring the nonlinear correlations between streamflow series. Subsequently, the complex network theory was applied to construct the streamflow similarity regionalization model, where the hydrological stations were set as nodes and the condition whether the R statistics of corresponding streamflow series were higher than the specified R-statistic threshold was considered as the basis for judging the existence of the linked edges between node pairs. The GN algorithm was used to conduct the streamflow similarity regionalization. Taking the hydrometric network of the Poyang Lake basin as an example, the research results showed that the streamflow similarity regionalization model was characterized by high stability and efficiency. When the R statistic was 0.8, the streamflow similarity regionalization result was the best, the hydrometric network was generally divided into two parts, i.e., north and south, and in total 12 types of regions, where the north part contained only one type of region. Compared with the K-means clustering method, the complex network method performs better, and its optimal partitioning result is more reasonable.