基于动力降尺度生成的中国未来气候数据（2040s，2060s，2080s）
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高精度的气候预测数据对于解决环境变化、城市规划和灾害管理方面的研究空白至关重要。本研究利用已经偏差校正后的18个CMIP6模型和ERA5的数据集合，使用WRF模型进行动力降尺度，开发了一个新的数据集。该数据集具有9公里的空间网格和15分钟的时间分辨率，涵盖了SSP245和SSP585排放情景下的2040年代、2060年代和2080年代的未来时期。它包括温度、降水量、辐射、风向和风速。相比起降尺度前的CMIP6模型，在温度与降雨的验证中表现较好，偏差显著低于其他CMIP6模型。并且该数据集集成了全球和区域天气模型，其他模型往往无法满足洪泛等城市水文过程的要求。其精细的时空降尺度提供了全面的气候预测工具，支持适应性城市设计和恢复力增强。高分辨率数据在城市水文、农业预测、城市规划、基础设施设计和灾害预警系统中具有重要应用，特别是在热浪等极端天气事件中，突出了其在科学决策和社会适应气候变化方面的作用。
Highly accurate climate projection data is critical for addressing research gaps in environmental change, urban planning, and disaster management. This study developed a novel dataset by dynamically downscaling an ensemble of 18 bias-corrected CMIP6 models and ERA5 reanalysis data using the WRF model. This dataset features a 9-kilometer spatial grid and 15-minute temporal resolution, covering future periods including the 2040s, 2060s, and 2080s under the SSP245 and SSP585 emission scenarios. It includes variables such as air temperature, precipitation, radiation, wind direction, and wind speed. Compared to the original CMIP6 models prior to downscaling, this dataset performs better in validation assessments for temperature and precipitation, with significantly lower biases than other CMIP6 models. Moreover, this dataset integrates both global and regional weather modeling frameworks, while most competing models often fail to meet the requirements of urban hydrological processes such as flood events. Its refined spatiotemporal downscaling yields a comprehensive climate projection toolkit that supports adaptive urban design and resilience enhancement. This high-resolution dataset has important applications in urban hydrology, agricultural forecasting, urban planning, infrastructure design, and disaster early warning systems. Notably, during extreme weather events such as heatwaves, it highlights its critical role in scientific decision-making and societal adaptation to climate change.
