ECHIDNA: Extreme Climate Historical and Future Indices Data under Numerous Approaches across Major Chinese River Basins Based on CMIP6 Multi-Model Ensemble (Part 3)

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，气候极端事件呈加剧态势，给生态系统、水资源与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区域尤为突出。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项极端气候指数检测专家组（ETCCDI）标准指数的综合数据库。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）的集合结果，通过CDFt、ECDFM、ISIMIP、LS、QDM、QM、SDM共7种统计降尺度方法进行处理。数据集的时间跨度为1979年至2100年，涵盖共享社会经济路径（SSP）下的三种情景：SSP1-2.6、SSP2-4.5与SSP5-8.5，研究区域聚焦四大流域：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）、克林-古普塔效率系数（KGE）与均方根误差（RMSE）开展评估，结果表明，相较于原始模式输出，该数据集在捕捉时间变异性与极端事件强度方面表现显著提升。通过整合多模式预测结果，ECHIDNA可对未来气候风险的不确定性进行可靠评估，为水文、农业及基础设施韧性规划提供支撑。该数据集公开获取，以助力气候影响研究、适应策略制定与国际科研合作。