Drought is a typical complex system, and nonlinear characteristics of drought are the concentrated reflection of its complexity. Therefore, objectively understanding of complex nonlinear characteristics of drought is the key approach of assessing the effects of drought, which can provide guideline for making drought mitigation strategies. Guanzhong Plain was chosen as study area, and the kernel method was applied as a nonlinear algorithm. Based on the kernel principal component analysis (KPCA), vegetation temperature condition index (VTCI) retrieved from MODIS was projected into a highdimensional feature space for feature extraction, and then the joint distribution model of principal components with Copula function was built. Comprehensive values of VTCIs at main growth stages from 2008 to 2013 were determined by using the joint distribution model (the KPCA—Copula method). Linear regression models between the comprehensive VTCIs and wheat yields were established to assess the effect of drought on wheat yields. The results showed that the KPCA could effectively extract the nonlinear characteristics of drought, and it had better performance in dimension reduction compared with the principal component analysis (PCA). Compared with the PCA—Copula method, the determination coefficient of regression model between wheat yields and comprehensive VTCIs with KPCA—Copula method reached 0.608 (p<0.001), which indicated that the fitting degree of the model was improved, and the root mean square error (RMSE) between estimated yields and measured ones was 298.1 kg/hm2, which was about 60.1kg/hm2 lower than the RMSE by using PCA—Copula method. The comprehensive VTCIs with KPCA—Copula method were more in line with actual drought characteristics of Guanzhong Plain. These results indicated that the KPCA—Copula method could well reflect nonlinear characteristics of drought, and it had good applicability in drought impact assessment.