Evidence map›Paper›PMID 39333116›Full record

ArticleScientific data2024

A high-resolution dataset for future compound hot-dry events under climate change.

Yizhuo Wen, Junhong Guo, Feng Wang, Zhenda Hao, Yifan Fei, Aili Yang, Yurui Fan, Faith Ka Shun Chan

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In one paragraph

Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yizhuo WenKey Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen, 361024, China.
Junhong GuoMOE Key Laboratory of Resource and Environmental, System Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing, 102206, China.
Feng WangState Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
Zhenda HaoSustainability Standards Research Center, School of Economics, Central University of Finance and Economics, Beijing, 100081, China.
Yifan FeiKey Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen, 361024, China.
Aili YangKey Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen, 361024, China.
Yurui FanDepartment of Civil and Environmental Engineering, Brunel University London, Uxbridge, London, UB8 3PH, Middlesex, UK. yurui.fan@brunel.ac.uk.ORCID http://orcid.org/0000-0002-0532-4026
Faith Ka Shun ChanSchool of Geographical Sciences, University of Nottingham Ningbo China, Ningbo, 315100, China.

Funding

China Postdoctoral Science Foundation 2023M730282Royal Society IES\R2\202075
6 · The paper itself

Abstract

Global climate change is leading to an increase in compound hot-dry events, significantly impacting human habitats. Analysing the causes and effects of these events requires precise data, yet most meteorological data focus on variables rather than extremes, which hinders relevant research. A daily compound hot-dry events (CHDEs) dataset was developed from 1980 to 2100 under various socioeconomic scenarios, using the latest NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) dataset to address this. The dataset has a spatial resolution of 0.25 degrees (approximately 30 kilometres), including three indicators, namely D (the yearly sum of hot-dry extreme days), prI (the intensity of daily precipitation), and tasI (the intensity of daily temperature). To validate the accuracy of the dataset, we compared observational data from China (National Meteorological Information Center, NMIC), Europe (ERA5), and North America (ERA5). Results show close alignment with estimated values from the observational daily dataset, both temporally and spatially. The predictive interval (PI) pass rates for the CHDEs dataset exhibit notably high values. For a 90% PI, D has a pass rate exceeding 85%, whilst prI and tasI respectively show a pass rate above 70% and 95%. These results underscore its suitability for conducting global and regional studies about compound hot-dry events.

Identifiers

PMID39333116
PMCPMC11437112

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.