Abstract: Objective pre-assessment of disaster risks using climate models is an emerging technology and operational practice. To evaluate spatiotemporal differences in pre-assessment results across various climate models and initial times, this study developed an objective quantitative verification system for disaster risk levels. Firstly, comparable databases of high-temperature and rainstorm events during flood seasons were constructed using observational data and outputs from Subseasonal-to-Seasonal Prediction Subsystem of the Third-Generation Climate Prediction System by China Meteorological Administration (CMA) (CMACPSv3-S2S) with monthly single-disaster risk spatial levels synthesized for verification. A five-dimensional quantitative evaluation method was then established, assessing event timing, duration, risk boundaries, highrisk zones, and historical rankings of total monthly risk. Additionally, the rainstorm risk assessment model' s accuracy was validated against county-level direct economic loss data. Finally, a comprehensive scoring method was then developed by integrating the five-dimensional evaluation metrics and model prediction accuracy. Results show that CMA-CPSv3-S2S exhibits strong skill in pre-assessing July rainstorms, with reliable performance within a two-week lead time. For June and August rainstorms, nowcasting yields better results. High-temperature pre-assessment skills in July and August surpass those in June, though high-risk zone localization remains challenging. From June to August 2024, the model's overall preassessment performance for rainstorm and high-temperature events exceeded its monthly median skill, except in a few initial periods. During 2008-2024, the flood season's monthly rainstorm risk preassessment scores were consistently near or above the multi-year average in 2024, with both the climate model and risk assessment model demonstrating relatively high skill. The proposed verification method enables cross-comparison of multi-model pre-assessment capabilities, providing robust support for disaster early warning and climate decision-making services.