Spatial and temporal patterns of precipitation concentration and their associated risks.

Xinhao Suo, Yongye Jiang, Guolong Chen, Jingyi Li, Yuqin Lai, Longsheng Li, Mengmeng Lu, Xinyi Wang, Bo Fu, Bengang Li

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Xinhao SuoInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Yongye JiangInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Guolong ChenInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Jingyi LiInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Yuqin LaiInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Longsheng LiInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Mengmeng LuInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Xinyi WangInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China.

Bo FuInstitute of Reproductive and Child Health, School of Public Health, Peking University, Beijing, 100191, China. pkufubo@pku.edu.cn.

Bengang LiInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China. libengang@pku.edu.cn.

Funding

Ministry of Science and Technology of the People's Republic of China 2022YFF0802503National Natural Science Foundation of China 42201515

6 · The paper itself

Abstract

Global climate change has profoundly altered precipitation distribution patterns, with the "Wet getting Wetter, Dry getting Drier" (WWDD) pattern widely recognized. However, most previous studies have addressed either the temporal or spatial dimensions of precipitation concentration in isolation, leaving a critical gap in understanding their finer-scale changes and their combined effects and interactive risks. Here, we use the Gini coefficient and ERA5-Land daily data (1951-2020) to assess global precipitation concentration in both time and space. Based on long-term trends, we classify regions into WWDD or WDDW patterns. Our results reveal that 46.4% of global land area shows increasing spatial concentration (WWDD), while 67.4% shows increasing temporal concentration (WWDD). Notably, 33.6% of the global land area exhibits a WWDD pattern in both spatial and temporal dimensions. These jointly concentrated regions are especially vulnerable, as precipitation is becoming more uneven in both time and space, increasing the risk of floods, droughts, and other extreme hydrometeorological events. In contrast, regions with increasing concentration in only one dimension may face more specific hazards-such as short-duration intense precipitation or localized runoff surges. These findings have important implications for assessing global precipitation concentration and informing hydrological disaster management strategies.

Identifiers

PMID41006813

DOI10.1038/s41598-025-18721-4

PMCPMC12474944

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

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.