Abstract: In this study we have developed a frequency analysis method of incomplete precipitation data series using Copula functions and examined its statistical properties by conducting Monte Carlo simulation tests. Using this method, we have built bivariate Frank Copula functions for six annual precipitation series observed in Shaanxi province and obtained a solution of the new parameters by applying a multivariable-constrained Rosenbrock optimization algorithm. To verify the method, Copula function-based simulations were conducted and evaluated using the criterion of minimum standard error of fit (SEF). The results show that the frequency of incomplete precipitation series calculated using the Copula functions has good statistical properties with all the statistics of simulation tests lower than critical values of the Anderson-Darling test at a significance level of 5%. The SEF of bivariate joint distribution is less than that of univariate distribution and the former’s estimation of design values is also superior. Frank Copula functions are generally effective and acceptable in analysis of precipitation frequency, and they are able to describe precipitation series better than the univariate methods, particularly for those design stations of incomplete series with difficult data extension.