With the rapid energy transition in power systems, the growing penetration of renewable energy and diversified loads has increased the complexity of grid operating scenarios. The deep coupling of multiple influencing factors among energy sources, loads, and environmental conditions further complicates scenario feature extraction. To address these challenges, this paper proposes an integrated wind-solar-load scenario generation method incorporating complex environmental coupling and multi-dimensional temporal features. First, considering strong temporal coupling and nonlinear characteristics of wind, solar, and load profiles, a bidirectional gated recurrent unit (Bi-GRU) is used to extract initial features from environmental factors and historical data, with feature vectors refined via error iteration. Subsequently, to accurately capture the probabilistic dependencies among wind, solar, load, and environmental variables, the extracted initial features are fed into a conditional variational autoencoder (CVAE) to construct a wind-solar-load scenario generation model. Then, to address the issue of unequal sequence lengths in wind-solar-load power curves, the dynamic time warping (DTW) algorithm is employed to reduce the generated scenarios, ensuring temporal consistency across the final output. Finally, the proposed method is validated using real-world wind-solar-load data from a region in China. The results demonstrate that the method can generate joint scenarios that more closely reflect real-world operating conditions.