Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from EC-Earth3 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.精准的积雪深度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至关重要。为构建面向北半球冬季的高分辨率逐月积雪深度数据集（Northern Hemisphere Monthly Snow Depth Dataset，简称NHMSD），本研究联合Delta统计降尺度方法（Delta statistical downscaling）与空间特征迁移技术，对CMIP6计划提供的21个主流全球环流模式（general circulation models）以及4组共享社会经济路径（shared socioeconomic pathways）的积雪深度数据开展精细化处理。NHMSD是全球首个长时序0.05°分辨率积雪深度数据集，涵盖1980-2014年的历史时段与2015-2100年的未来预估时段。本研究利用2062组地面积雪深度观测数据开展验证，结果表明，在1980-2014年与2015-2023年两个时段中，NHMSD在均方根误差、偏差以及平均绝对误差三项指标上均优于ERA5-Land、GLDAS等再分析数据集。