Evidence map›Paper›PMID 39939321›Full record

ArticleScientific data2025

A global high-resolution and bias-corrected dataset of CMIP6 projected heat stress metrics.

Qinqin Kong, Matthew Huber

Abstract read
In one paragraph

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 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Qinqin KongDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, 47907, United States of America. kong97@purdue.edu.ORCID http://orcid.org/0000-0002-4593-3643
Matthew HuberDepartment of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, 47907, United States of America.ORCID http://orcid.org/0000-0002-2771-9977

Funding

National Aeronautics and Space Administration (NASA) 80NSSC22K1544National Science Foundation (NSF) 1805808-CBETNational Science Foundation (NSF) 1829764-OAC
6 · The paper itself

Abstract

Increasing heat stress with climate change will threaten human health and cause broad social and economic impacts. The evaluation of such impacts depends on a reliable dataset of heat stress projection. Here we present a global dataset of the future projection of dry-bulb, wet-bulb and wet-bulb globe temperature under 1-4°C of global warming levels compared with the preindustrial era using output from 16 CMIP6 global climate models (GCMs). The dataset was bias-corrected against ERA5 reanalysis by adding the GCM-simulated climate change signal onto ERA5 baseline (1950-1976) at 3-hourly frequency. The resulting datasets are provided at fine spatial (0.25° × 0.25°) and temporal (3-hourly) resolution. We validate the bias-correction approach and demonstrate that it substantially improves the GCMs' ability to reproduce both the annual average and entire range of quantiles for all metrics within an ERA5 reference climate state. We expect the dataset to benefit future work on estimating projected changes in both mean and extreme heat stress and assessing consequential health and social-economic impacts.

Identifiers

PMID39939321
PMCPMC11821900

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LicenceCC BY-NC-ND
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Registered trials

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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.