Gaussian process emulator with separable covariance function has been utilized extensively in modeling large computer model outputs. The assumption of separability imposes constraints on the emulator and may negatively affect its performance in some applications where separability may not hold. We propose a multi-output Gaussian process emulator with a nonseparable auto-covariance function to avoid limitations of using separable emulators. In addition, to facilitate the computation of nonseparable emulator, we introduce a new computational method, referred to as the Full-Scale approximation method with block modulating function (FSA-Block) approach. The FSA-Block is an effective and accurate covariance approximation method to reduce computations for Gaussian process models, which applies to both nonseparable and partially separable covariance models. We illustrate the effectiveness of our method through simulation studies and compare it with emulators with separable covariances. We also apply our method to a real computer code of the carbon capture system.