In the context of large-scale integration of wind and solar power into the grid, power system dispatch strategies faced unprecedented challenges. The volatility and randomness of wind and photovoltaic power generation significantly impacted system stability and controllability. To accurately characterize the spatiotemporal correlation of wind-solar power output and construct a practically valuable scenario set, a method for generating spatiotemporal correlated scenarios for wind-solar complementary systems was proposed, based on a coupled SGMM (seasonal Gaussian mixture model) and MCopula (mixed Copula function). Initially, the SGMM was constructed to capture the temporal correlation among wind-solar output variables. Then, the mixed Copula function was employed to describe the spatial correlation among variables. Based on the comprehensive modeling of spatiotemporal correlations, a series of uncertainty scenario sets reflecting these characteristics was generated using the Copula conditional distribution function and inverse transform sampling technique. The simulation results confirmed the effectiveness and reliability of the proposed method. The generated scenario sets not only reflected the spatiotemporal correlation characteristics and annual variation trends of wind-solar output but also better matched the historical actual sequences in terms of distance, providing strong decision-making support for power system dispatch. New perspectives and tools were offered for quantifying uncertainties in wind-solar complementary systems, which had profound theoretical and practical significance for optimizing power system dispatch strategies, reducing uncertainty risks, promoting the efficient utilization of renewable energy, and advancing the sustainable development of power systems.