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Drought is one of the most important agricultural disasters in the Guanzhong Plain, China. Assessing the influence of the droughts in the plain accurately can provide reference for drought mitigation and maintaining stable crop yields. Based on remotely sensed vegetation temperature condition index (VTCI) which was calculated at tenday intervals for monitoring droughts in the years of 2008—2013 in the plain, the Copula function method was used to assess the effect of drought at the main growth stages of winter wheat on the yields. The mutually independent principal factors were extracted from the VTCIs at the main growth stages of winter wheat by using principal component analysis (PCA), overcoming difficulty of parameter estimation for multivariate Copula, and then incorporated into the Copula function to establish a PCA-Copula method. The comprehensive values of VTCIs at the main growth stages were determined by the PCA-Copula method, and then linear regression model between the comprehensive VTCIs and wheat yields was established to assess the effect of drought on the yields. The results showed that the linear correlation coefficient between the wheat yields and comprehensive VTCIs was at the extremely significant level (P<0.001). Compared with the linear regression model based on the entropy value method, the determination coefficient of the model with the PCA-Copula method reached 0.49 from 0.39, which indicated that the fitting degree of the model was improved, and the root mean square error and average relative error between the estimated and measured yields reduced by 30.2kg/hm2 and 0.66%, respectively. These results indicated that the PCA-Copula method was a better approach for accessing the impact of droughts at the main growth stages of winter wheat on the yield.