Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from BCC-CSM2-MR model

Accurate snow depth datasets are of paramount importance for water resource management, comprehensive climate change assessments, and the sustainable development of the ice-and-snow economy. To create a high-resolution monthly snow depth dataset tailored for the Northern Hemisphere winter months (NHMSD), this study employed the Delta statistical downscaling method, in conjunction with a spatial feature transfer technique, to refine snow depth data derived from 21 major general circulation models and four shared socioeconomic pathways sourced from the CMIP6 project. The NHMSD stands as the world's pioneering long-term 0.05° snow depth dataset, encompassing the historical era from 1980 to 2014 and extending into future projections from 2015 to 2100. Validation using 2062 ground snow depth observations has confirmed that NHMSD outperforms reanalysis datasets, including ERA5-Land and GLDAS, in terms of root mean square error, bias, and mean absolute error for the periods 1980–2014 and 2015–2023.精准的雪深数据集对于水资源管理、全面的气候变化评估以及冰雪经济的可持续发展均具有至关重要的意义。为构建适配北半球冬季的高分辨率逐月雪深数据集（NHMSD），本研究采用Delta统计降尺度方法（Delta statistical downscaling method）结合空间特征迁移技术（spatial feature transfer technique），对来自耦合模式比较计划第六阶段（CMIP6）的21个主流全球环流模式（general circulation models）与4条共享社会经济路径（shared socioeconomic pathways）下的雪深数据进行精细化校正。NHMSD是全球首个长期0.05°分辨率雪深数据集，时间跨度涵盖1980至2014年的历史时段与2015至2100年的未来预估时段。通过2062个地面雪深观测点数据开展验证，结果显示在1980-2014年与2015-2023年两个时段内，NHMSD在均方根误差（root mean square error）、偏差（bias）与平均绝对误差（mean absolute error）三项指标上均优于第五代陆面再分析数据集（ERA5-Land）、全球陆面数据同化系统（GLDAS）等主流再分析数据集。