Evidence map›Paper›PMID 40447807›Full record

ArticleScientific reports2025

Historical and projected extreme climate changes in the upper Yellow River Basin, China.

Shihao Chen, Baohui Men, Jinfeng Pang, Zongzhen Bian, Hongrui Wang

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Shihao ChenSchool of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, 102206, China.
Baohui MenSchool of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, 102206, China. menbh@ncepu.edu.cn.
Jinfeng PangSchool of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, 102206, China.
Zongzhen BianSchool of Water Resources and Hydropower Engineering, North China Electric Power University, Beijing, 102206, China.
Hongrui WangCollege of Water Sciences, Beijing Normal University, Beijing, 100875, China.

Funding

National Natural Science Foundation of China 52279005
6 · The paper itself

Abstract

Considering plateau climate and complex terrain of the upper Yellow River Basin, understanding changes in climate extremes has become increasingly urgent. This study has highlighted the historical changes in climate extremes from 1960 to 2022 based on 20 extreme climate indices, and future changes in climate extremes until 2100 under two Shared Socioeconomic Pathways (SSP126 and SSP585) based on the Coupled Model Intercomparison Project phase 6 (CMIP6) models. We found that historical spatial and temporal evolutions of precipitation extremes (PEs) and temperature extremes (TEs) primarily exhibit increasing trends. The frequency and intensity of PEs primarily show an increasing trend, while the duration of PEs primarily shows a decreasing trend. Both the frequency and duration of cold extremes primarily show a decreasing trend, while the intensity of cold extremes, as well as the intensity, frequency, and duration of warm extremes, primarily show an increasing trend. Future PEs and TEs are expected to continue to intensify even under the most ideal scenario (i.e., SSP126), and these are anticipated to further intensify with increasing radiative forcing levels and greenhouse gas concentrations. Results could provide scientific references for better coping with extreme climate changes in regions of complex terrain and scarce observation station.

Indexed as

CMIP6Precipitation extremesTemperature extremesThe upper yellow river basin

Identifiers

PMID40447807
PMCPMC12125197

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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.