A 1 km monthly dataset of historical and future climate changes over China.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-13 DOI:10.1038/s41597-025-04761-y

Xiaofei Hu, Shaolin Shi, Borui Zhou, Jian Ni

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引用次数: 0

Abstract

High-resolution climate data are important for understanding the impacts of climate change on multiple sectors worldwide. In this study, based on the latest released meteorological records during 1991-2020 and the recently updated general circulation models (GCMs), we established a 30-year averaged 0.01° (≈1 km) dataset of 5 basic climate variables and 23 bioclimatic variables, using ANUSPLIN software, delta correction (DC) downscaling, and cubic spline resampling method. Each variable contained monthly gridded historical data during 1991-2020 and bias-corrected future data over three periods (2021-2040, 2041-2070, 2071-2100), three scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) and 10 GCMs (including an ensemble model). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The DC correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%-58.63%. This new dataset therefore demonstrates reliable data quality, and further provides high-resolution and bias-corrected long-term averaged historical and future climate data across China for ecological and climate impact studies.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.