Abstract: The trend-caused nonstationarity of the hydrometeorological time series in the context of climate change will pose a signifiacnt impact on the optimization results of the hydrometeorological gauge network. This paper establishes a C-Vine copula entropy-based multi-objective gauge network optimization model to analyze the daily precipitation time series of the Yellow River and the Huaihe River Basin.Through the constructed optimization model, the rank of each station in two basins under different window width conditions is analyzed, and the influence of the trend degree of rainfall series on the optimization results is analyzed quantitatively. The results show that because Archimedean Copula is only suitable for describing the multivariable dependence structure of positive correlation, C-Vine Copula Model is able to achieve the better estimation of total correlation, which is just the actual network information redundancy, especially in the case of high dimension. The annual variation of rank of the Huaihe River Basin with a larger trend is more significant than that of the Yellow River Basin station network with small trend degree, and the trend-caused nonstationarity of the series increases the uncertainty of the evaluation results of hydrometeorological station network.Different time domain ranges may have different effects on the station network design results, which means that the best network design may only be applicable to specific observation periods. Therefore, this study shows that it should be very cautious in dealing with the optimization of hydrometeorological gauge network, because under the other identical conditions, the elimination or addition of some stations based on the fixed whole series will lead to the loss or redundancy of hydrometeorological information of the whole gauge network. The optimal design of station network should make it more suitable for the changing hydrometeorological conditions.