Owing to the uncertainty and volatility of wind energy, forecasted wind power scenarios with proper spatio-temporal correlations are needed in various decision-making problems involving power systems. In this study, forecasted scenarios are generated from an estimated multi-variate distribution of multiple regional wind farms. According to the theory of copulas, marginal distributions and the dependence structure of multi-variate distribution are modeled through the proposed distance-weighted kernel density estimation method and the regular vine (R-vine) copula, respectively. Owing to the flexibility of decomposing correlations of high dimensions into different types of pair-copulas, the R-vine copula provides more accurate results in describing the complicated dependence of wind power. In the case of 26 wind farms located in East China, high-quality forecasted scenarios as well as the corresponding probabilistic forecasting and point forecasting results are obtained using the proposed method, and the results are evaluated using a comprehensive verification framework.