Evidence map›Paper›PMID 38844448›Full record

ArticleNature communications2024

Global expansion of tropical cyclone precipitation footprint.

Lianjie Qin, Laiyin Zhu, Baoyin Liu, Zixuan Li, Yugang Tian, Gordon Mitchell, Shifei Shen, Wei Xu, Jianguo Chen

Abstract read
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Tropical cyclone rainfall extends inland.Nature communications · 2026
    Article
  4. Article
  5. Tropical cyclones expand faster at warmer relative sea surface temperature.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  6. Article
  7. Article
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

9 authors.

Lianjie Qin *School of Safety Science, Tsinghua University, Beijing, China.ORCID http://orcid.org/0000-0002-3581-3752
Laiyin Zhu *School of Environment, Geography, and Sustainability, Western Michigan University, Kalamazoo, MI, USA. laiyin.zhu@wmich.edu.ORCID http://orcid.org/0000-0003-3612-7583
Baoyin LiuInstitutes of Science and Development, Chinese Academy of Sciences, Beijing, China.ORCID http://orcid.org/0000-0003-3370-7646
Zixuan LiSchool of Finance, Nankai University, Tianjin, China.ORCID http://orcid.org/0000-0001-8202-9648
Yugang TianSchool of Geography and Information Engineering, China University of Geosciences, Wuhan, China.
Gordon MitchellSchool of Geography and Water@leeds, University of Leeds, Leeds, UK.ORCID http://orcid.org/0000-0003-0093-4519
Shifei ShenSchool of Safety Science, Tsinghua University, Beijing, China.
Wei XuKey Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing, China. xuwei@bnu.edu.cn.ORCID http://orcid.org/0000-0002-6912-9000
Jianguo ChenSchool of Safety Science, Tsinghua University, Beijing, China. chenjianguo@tsinghua.edu.cn.ORCID http://orcid.org/0000-0002-5289-4216

Funding

National Natural Science Foundation of China (National Science Foundation of China) 72293571National Natural Science Foundation of China (National Science Foundation of China) U22B2011
6 · The paper itself

Abstract

Precipitation from tropical cyclones (TCs) can cause massive damage from inland floods and is becoming more intense under a warming climate. However, knowledge gaps still exist in changes of spatial patterns in heavy TC precipitation. Here we define a metric, DIST30, as the mean radial distance from centers of clustered heavy rainfall cells (> 30 mm/3 h) to TC center, representing the footprint of heavy TC precipitation. There is significant global increase in DIST30 at a rate of 0.34 km/year. Increases of DIST30 cover 59.87% of total TC impact areas, with growth especially strong in the Western North Pacific, Northern Atlantic, and Southern Pacific. The XGBoost machine learning model showed that monthly DIST30 variability is majorly controlled by TC maximum wind speed, location, sea surface temperature, vertical wind shear, and total water column vapor. TC poleward migration in the Northern Hemisphere contributes substantially to the DIST30 upward trend globally.

Identifiers

PMID38844448
PMCPMC11156673

What OpenQuestion holds

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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.