Abstract: The operation and planning of power systems with high penetrations of renewable energy highly depend on high-temporal-resolution meteorological data and wind-solar power output scenarios. However, most existing climate models only provide daily-scale meteorological information, which is insufficient to directly support the hourly-level operational analysis of power systems. Therefore, this paper proposes a joint scenario generation method for meteorology-wind-solar power outputs based on retrieval-augmented probabilistic downscaling (RAPD). First, a similar-day retrieval mechanism based on the rank-sum distance is constructed to extract historical high-resolution meteorological baseline sequences. Second, a Transformer-based conditional variational autoencoder (T-CVAE) learning network is built to realize the probabilistic generation of meteorological fluctuation residuals. Finally, a physical mapping model from meteorological variables to wind-solar power outputs is established to generate continuous hourly wind-solar power output scenarios for a whole year. Validations based on historical meteorological data demonstrate that the mean absolute error (MAE) values for temperature and wind speed are reduced by 30.17% and 28.84%, respectively, compared with the traditional statistical analog method. Furthermore, taking the daily-scale data from climate models as input, the corresponding hourly-level meteorology-wind-solar power output scenarios are generated through downscaling, and the consistency between the downscaled results and the daily-scale inputs is verified. The results indicate that the proposed method can effectively accomplish temporal downscaling and scenario generation, providing a high-temporal-resolution scenario foundation for renewable energy output analysis under future scenarios.