Kriging model is widely used as design analysis and computer experiment (DACE) model in the field of engineering design to accomplish computationally feasible design optimization. In general, kriging model has been applied to many engineering applications as an interpolation model because it is usually constructed from deterministic simulation responses. However, when the responses include not only global nonlinearity but also numerical error, it is not suitable to use Kriging model that can distort global behavior. In this research, generalized kriging model that can represent both interpolation and regression is proposed. The performances of generalized kriging model are compared with those of interpolating kriging model for numerical function with error of normal distribution type and trigonometric function type. As an application of the proposed approach, the response of a simple dynamic model with numerical integration error is predicted based on sampling data. It is verified that the generalized kriging model can predict a noisy response without distortion of its global behavior. In addition, the influences of maximum likelihood estimation to prediction performance are discussed for the dynamic model.