<p>The wind–solar scenario generation method can effectively capture the joint variability of wind and solar resources to enhance uncertainty representation. In this study, we introduce a novel integrated wind–solar scenario generation method considering seasonal characteristics. Firstly, we propose a seasonally adaptive distribution model to describe the stochastic and seasonal characteristics of wind and solar irradiance. Additionally, a set of wind–solar scenarios with spatial correlation were generated using Frank–Copula functions, and the K-means clustering algorithm was used to obtain typical scenarios for every season. The southeast coastal region of China was selected as a case study to comprehensively evaluate the effectively of the proposed scenario generation method. The results indicate that both wind speed and irradiance exhibit distinct seasonal distribution patterns: wind speed follows the Weibull distribution in the spring, the Generalized Extreme Value distribution (GEV) distribution in the summer and autumn, and the Gamma distribution in the winter, while irradiance is best characterized by the Beta distribution in the spring, summer, and autumn and the Gamma distribution in the winter. Compared with methods that rely on a single distribution, the generated wind–solar joint scenarios allow a 30.4% reduction in the mean Euclidean distance (MED) of wind speed, an 8.7% reduction in the MED of irradiance, and a 6.6% reduction in the mean absolute error of the Kendall correlation coefficient (KMAE).</p>