ArticleScientific data2025
A 1 km monthly dataset of historical and future climate changes over China.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Using Biomod2 and spatial quality modeling for comprehensive production zoning ofFrontiers in plant science · 2026Article
- TSWS: An observation-based streamflow dataset of Tianshan Mountains watersheds (1901-2019).Scientific data · 2025Article
- A 1 km monthly dataset of historical and future climate changes over China.Scientific data · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
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