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Weather generators (WGs) are often used to develop an ensemble of plausible climate scenarios across multiple spatial and temporal scales for vulnerability assessment and impact studies. Most of the conventional WGs are single-site models, which neglect the spatial dependence in the simulated meteorological field, are improper for regional impact studies. In recent several decades, efforts have been devoted to develop multivariate, multisite weather generators (MMWGs), which are able to reproduce the inter-variable and inter-site dependencies as well as the temporal structures observed in the historical records. Though several improvements have been achieved, the existed MMWGs are either conceptually complex or computationally expensive, and have difficulty in preserving the entire desired attributes. This study proposes a new two-stage scheme for constructing a MMWG. At the first stage, the meteorological series are simulated from a single-site multivariate weather generator (SMWG), which preserves the marginal distributional attributes for each meteorological variable. For the second stage, the inter-variable and inter-site dependencies as well as the temporal structures are reproduced using the Empirical Copula (EC) approach. An application of the proposed MMWG is presented for the Upper Thames River Catchment, Canada, and the performance is evaluated and compared with another two-stage MMWG based on the Iman shuffle approach. Results show that the proposed MMWG not only preserves the marginal distributional attributes, but also restores the inter-variable and inter-site dependencies, the temporal persistence, and the inter-annual variability almost perfectly. The performance of the proposed MMWG outperforms the one based on the Iman shuffle approach in terms of reconstruction of temporal persistence and inter-annual variability, as well as the reproduction of multivariate dependencies. Being conceptually simple and computationally inexpensive, the proposed scheme based on the EC approach is well suited for developing ensemble-based climate scenarios for vulnerability assessment and impact studies.