Evidence map›Paper›PMID 42428801›Full record

ReviewNPJ climate and atmospheric science2026

New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations.

Kwok Pan Chun, Leonardo Aragão, Matías Ezequiel Olmo, Viet Dung Nguyen, Christoforus Bayu Risanto, Maria Laura Bettolli, Yasemin Ezber, Emir Toker, Konstantinos V Varotsos

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in NPJ climate and atmospheric science, 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

9 authors.

Kwok Pan ChunUniversity of the West of England, Bristol, UK.
Leonardo AragãoCMCC Foundation - Euro-Mediterranean Center on Climate Change, Bologna, Italy.
Matías Ezequiel OlmoBarcelona Supercomputing Center, Barcelona, Spain.
Viet Dung NguyenGFZ Helmholtz Centre for Geosciences, Section Hydrology, Potsdam, Germany.
Christoforus Bayu RisantoVatican Observatory, Vatican City State, Italy.
Maria Laura BettolliUniversity of Buenos Aires, Buenos Aires, Argentina.
Yasemin EzberEurasia Institute of Earth Sciences, Istanbul Technical University, 34469 Istanbul, Türkiye.
Emir TokerEurasia Institute of Earth Sciences, Istanbul Technical University, 34469 Istanbul, Türkiye.
Konstantinos V VarotsosInstitute for Environmental Research and Sustainable Development, National Observatory of Athens, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial intelligence (AI) enable high-resolution, probabilistic, and computationally efficient approaches. This paper provides a perspective on the evolution from classical to AI-driven and hybrid downscaling approaches, assesses key challenges related to interpretability, uncertainty, data availability, and computational requirements, and outlines physically constrained and generative frameworks that support decision-making across sectors.

Indexed as

Climate sciencesEnvironmental sciencesHydrologyMathematics and computingWater resources

Identifiers

PMID42428801
PMCPMC13345914

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.