ECHIDNA: Extreme Climate Historical and Future Indices Data under Numerous Approaches across Major Chinese River Basins Based on CMIP6 Multi-Model Ensemble (Part 4)

Climate extremes are intensifying under global warming, posing unprecedented challenges to ecosystems, water resources, and human societies. However, high-resolution, basin-specific extreme climate datasets remain scarce, particularly in climatically diverse regions like China. Here, we present ECHIDNA (Extreme Climate Historical and Future Indices Data under Numerous Approaches), a comprehensive database of 33 ETCCDI indices derived from an ensemble of eight CMIP6 Global Climate Models (GCMs), statistically downscaled using seven methods including CDFt, ECDFM, ISIMIP, LS, QDM, QM, and SDM. Covering 1979–2100 under three SSP scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), the dataset focuses on four major river basin regions: the Yangtze River Basin, Yellow River Basin, Hai River Basin, and Southwest River Basins. Evaluation using PCC, NSE, KGE, and RMSE demonstrates significant improvements in capturing temporal variability and extreme event intensity compared to raw model outputs. By incorporating multi-model projections, ECHIDNA enables robust assessments of uncertainty in future climate risks and supports hydrological, agricultural, and infrastructure resilience planning. It is openly available to facilitate climate impact studies, adaptation strategies, and international research collaboration.全球变暖背景下，极端气候事件愈发频发，给生态系统、水资源以及人类社会带来了前所未有的挑战。然而，高分辨率、针对特定流域的极端气候数据集依然稀缺，在中国这类气候多样的地区尤为如此。在此，我们发布ECHIDNA（多方法下的极端气候历史与未来指数数据集，Extreme Climate Historical and Future Indices Data under Numerous Approaches），这是一套涵盖33项极端气候检测与指数专家组（ETCCDI）指数的综合数据库。该数据集基于8个耦合模式比较计划第六阶段（CMIP6）全球气候模式（GCMs）的集合模拟结果，通过CDFt、ECDFM、ISIMIP、LS、QDM、QM及SDM共7种方法完成统计降尺度处理。该数据集的时间跨度为1979年至2100年，涵盖SSP1-2.6、SSP2-4.5及SSP5-8.5共3种共享社会经济路径（SSP）情景，研究区域涵盖四大流域：长江流域、黄河流域、海河流域以及西南诸河流域。通过皮尔逊相关系数（PCC）、纳什效率系数（NSE）、Kling-Gupta效率系数（KGE）及均方根误差（RMSE）开展评估，结果表明，相较于原始模式输出结果，该数据集在捕捉时间变异性与极端事件强度方面均有显著提升。通过整合多模式预估结果，ECHIDNA可对未来气候风险的不确定性开展稳健评估，并为水文、农业及基础设施韧性规划提供支撑。该数据集公开可获取，以助力气候影响研究、适应策略制定及国际科研合作。