Evidence map›Paper›PMID 42202022›Full record

ArticleScience advances2026

Extreme-range precipitation probability across global weather systems.

Suqin Q Duan, Wei-Ming Tsai, Fiaz Ahmed, J David Neelin

Abstract read
In one paragraph

Article in Science advances, 2026. 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

4 authors.

Suqin Q DuanDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-6951-119X
Wei-Ming TsaiDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0002-0248-7233
Fiaz AhmedDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.
J David NeelinDepartment of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.ORCID 0000-0001-9414-9962

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precipitating weather systems are major contributors to hazardous extreme rain, yet weather and climate models struggle to capture their dynamics and relative impacts. Using high-resolution observations, we examine precipitation probability distributions at hourly timescale linked to major system types across the globe. We show that the high-intensity tails of these distributions share a typical shape, which can be characterized by an intensity scale. Dynamic processes largely govern this universal shape, while thermodynamic factors modulate its magnitude. We find that intensity scales of major contributing systems largely match those of total precipitation due to shared thermodynamic environments, dominance of intense systems, and modest variations in dynamic scales. This universality of various system types' precipitation probability tail supports risk assessment without overly depending on weather-type identification for most regions. We identify exceptions where intense tropical low-pressure systems or mesoscale convective system co-occurrences disproportionately affect extremes, yielding gray-swan-prone distributions.

Identifiers

PMID42202022
PMCPMC13215167

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.