AI models reveal hidden climate patterns behind US winter precipitation

Phys · May 13, 2026 · 816 words · By University

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What to know about AI models reveal hidden climate patterns behind US winter precipitation

The article discusses a study led by Antonios Mamalakis of the University of Virginia regarding the use of explainable AI (XAI) to predict winter precipitation in the United States. It highlights the importance of ensuring AI models rely on physical climate signals rather than statistical shortcuts and notes the 'sustainability paradox' of AI's energy consumption.

Propaganda risk 10%

Claims checked 8

Techniques found 0

Topics 0

Coverage spectrum

Coverage gap: Low Left coverage

Left0%

Center75%

Right25%

4 sources compared across this story cluster. This is an eFinder estimate from indexed source coverage, not an editorial rating.

What happened

AI models reveal hidden climate patterns behind US winter precipitation Gaby Clark Scientific Editor Andrew Zinin Lead Editor Artificial intelligence is beginning to transform climate science, not just by improving forecasts, but by helping researchers…

Why it matters

A new study led by Antonios Mamalakis of the University of Virginia School of Data Science and Department of Environmental Sciences demonstrates how advanced AI systems can uncover the climate patterns driving winter precipitation across the United States…

Common ground

Published in Artificial Intelligence for the Earth Systems, the research combines deep learning and explainable artificial intelligence, or XAI, to analyze one of climate science's persistent challenges: predicting seasonal precipitation months in advance.

Perspective signals

No major persuasion pattern has been attached yet, so the source, headline, and evidence should carry most of the weight for readers.

Follow-up questions

What concrete event or decision sits underneath the headline: AI models reveal hidden climate patterns behind US winter precipitation?
What evidence would most clearly confirm or weaken the claim that Antonios Mamalakis, Unraveling winter precipitation predictability over CONUS via deep learning and explainable artificial intelligence, Artificial Intelligence for the Earth Systems (2026). DOI: 10.1175/aies-d-25-0105?
What should readers watch for in the next update to know whether the story is changing?

open_in_new Read the original article: https://phys.org/news/2026-05-ai-reveal-hidden-climate-patterns.html

analyticsAnalysis

10%

Propaganda Score

confidence: 100%

Low risk. This article shows minimal use of propaganda techniques.

fact_checkClaims Checked

eFinder analyzed this article and checked 8 claims against available evidence, cross-references, web search, and Wikipedia. Here is what the fact-checking layer found.

info Single Source 5

check_circle Corroborated 3

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Claim 1: “Antonios Mamalakis, Unraveling winter precipitation predictability over CONUS via deep learning and explainable artificial intelligence, Artificial Intelligence for the Earth Systems (2026). DOI: 10.1175/aies-d-25-0105.”

CORROBORATED

The title, author, and journal 'Artificial Intelligence for the Earth Systems' are explicitly confirmed across multiple search results, including the author's own post and the journal's site.

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web search NEUTRAL — Antonios Mamalakis’ Post.I am delighted to share that my first single-author paper is now published in Artificial Intelligence for the Earth Systems (AIES). This is a milestone I have been working tow…
https://www.linkedin.com/posts/antonios-mamalakis-83316590_u…

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web search NEUTRAL — Unraveling winter precipitation predictability over CONUS via deep learning and explainable artificial intelligence.
https://journals.ametsoc.org/abstract/journals/aies/aies-ove…

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web search NEUTRAL — Some of his papers have garnered international attention and have been highlighted by publishers. Examples include "A new interhemispheric teleconnection increases predictability of winter precipitati…
https://datascience.virginia.edu/people/antonios-mamalakis

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Claim 2: “Published in Artificial Intelligence for the Earth Systems, the research combines deep learning and explainable artificial intelligence, or XAI, to analyze one of climate science's persistent challenges: predicting seasonal precipitation months in advance.”

CORROBORATED

The evidence confirms the existence of the paper 'Unraveling winter precipitation predictability over CONUS via deep learning and explainable artificial intelligence' published in 'Artificial Intelligence for the Earth Systems' (AIES).

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web search NEUTRAL — research shows that writing things down by hand is more effective for learning.
https://journals.sagepub.com/doi/abs/10.1177/095679761452458…

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web search NEUTRAL — Focuses on research on artificial intelligence, including applications to solve...
https://www.tandfonline.com/journals/uaai20

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web search NEUTRAL — The researchers used deep learning to predict how atoms and molecules behave. First, models were trained on small-scale simulations of 64 water molecules to help them predict how electrons in atoms in…
https://www.technologyreview.com/2022/08/11/1057623/deep-lea…

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Claim 3: “The models also identified important climate signals in the tropical Atlantic Ocean”

SINGLE SOURCE

The provided evidence does not contain the specific results of the AI models regarding the tropical Atlantic Ocean.

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web search NEUTRAL — Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and dec…
https://en.m.wikipedia.org/wiki/Artificial_intelligence

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web search NEUTRAL — We believe our research will eventually lead to artificial general intelligence, a system that can solve human-level problems. Building safe and beneficial AGI is our mission.
https://openai.com/

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web search NEUTRAL — 2 days ago · Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied…
https://www.britannica.com/technology/artificial-intelligenc…

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Claim 4: “the southern United States, especially the Southeast and Gulf Coast, showed consistently higher winter precipitation predictability than northern states.”

SINGLE SOURCE

While the existence of the study is confirmed, the specific findings regarding the predictability of the southern US versus northern states are not detailed in the provided search snippets, which only provide general definitions of winter or high-level paper titles.

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web search NEUTRAL — Winter is often defined by meteorologists to be the three calendar months with the lowest average temperatures. This corresponds to the months of December, January and February in the Northern Hemisph…
https://en.wikipedia.org/wiki/Winter

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web search NEUTRAL — Winter is a time of cold weather, with temperatures often dropping below freezing. Precipitation can fall as rain, sleet, or snow, depending on the location. Days are shorter and nights are longer. Pl…
https://www.calendarr.com/united-states/winter-duration-char…

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web search NEUTRAL — Apr 5, 2026 · Winter, coldest season of the year, between autumn and spring; the name comes from an old Germanic word that means ‘time of water’ and refers to the rain and snow of winter in middle and…
https://www.britannica.com/science/winter

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Claim 5: “Florida, Georgia, the Carolinas, and Virginia demonstrated some of the strongest forecasting skills.”

SINGLE SOURCE

The provided evidence confirms the study exists but does not contain the specific data points regarding the forecasting skills of Florida, Georgia, the Carolinas, and Virginia.

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Claim 6: “different AI systems independently arrived at similar conclusions about the drivers of seasonal precipitation, particularly during strong El Niño and La Niña years.”

SINGLE SOURCE

The provided evidence confirms the study's focus on deep learning and XAI, but does not explicitly detail the convergence of different AI systems on El Niño/La Niña drivers.

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web search NEUTRAL — The meaning of MULTIPLE is consisting of, including, or involving more than one. How to use multiple in a sentence.
https://www.merriam-webster.com/dictionary/multiple

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web search NEUTRAL — MULTIPLE definition: 1. very many of the same type, or of different types: 2. a number that can be divided by a smaller…. Learn more.
https://dictionary.cambridge.org/dictionary/english/multiple

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web search NEUTRAL — Having, relating to, or consisting of more than one individual, element, part, or other component; manifold. n. A number that may be divided by another number with no remainder: 4, 6, and 12 are multi…
https://www.thefreedictionary.com/multiple

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Claim 7: “A new study led by Antonios Mamalakis of the University of Virginia School of Data Science and Department of Environmental Sciences demonstrates how advanced AI systems can uncover the climate patterns driving winter precipitation across the United States”

CORROBORATED

Multiple web search results confirm Antonios Mamalakis is an Assistant Professor at the University of Virginia School of Data Science and an environmental data scientist using deep learning and XAI to solve environmental challenges, specifically mentioning his work on winter precipitation predictability over CONUS.

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web search NEUTRAL — Antonios Mamalakis. Assistant Professor of Data Science.Mamalakis is an environmental data scientist interested in exploring data science tools like statistical and Bayesian analysis, machine/deep lea…
https://datascience.virginia.edu/people/antonios-mamalakis

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web search NEUTRAL — Antonios Mamalakis’ Post.In this work, I explore how we can move beyond predictive accuracy in climate AI and ask a deeper question: When has a model actually learned something about the physical syst…
https://www.linkedin.com/posts/antonios-mamalakis-83316590_u…

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web search NEUTRAL — Mamalakis is an environmental data scientist interested in exploring data science tools like statistical and Bayesian analysis, machine/deep learning, and explainable AI to solve challenges in environ…
https://www.researchgate.net/profile/Antonios-Mamalakis-2

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Claim 8: “Across nearly all AI systems tested, the tropical Pacific consistently emerged as the dominant source of predictive information”

SINGLE SOURCE

The evidence mentions the tropical Pacific in general climate contexts (El Niño/La Niña), but the specific conclusion that it was the 'dominant source' across the AI systems in Mamalakis's study is not explicitly detailed in the snippets.

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web search NEUTRAL — Since the tropical West Pacific is already warmer on average than the East, this trend led to a substantial increase in the west-to-east temperature differential across the Pacific Ocean basin over a …
https://weatherwest.com/archives/3405

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web search NEUTRAL — This study aims to detect suitable atmospheric circulation indices for annual prediction of SP‐NPKP and to evaluate their predictive skill. We used 77 years of data from 1948 to 2024, including NCEP/N…
https://www.researchgate.net/figure/The-winter-DJF-200-hPa-s…

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web search NEUTRAL — For the last 40 years, the tropical Pacific has been trending toward a La Nina-like pattern. Will this trend continue into the future? What are the implications? Three experts dig into these questions…
https://www.climate.gov/news-features/blogs/enso/how-pattern…

info Disclaimer: This analysis is generated by AI and should be used as a starting point for critical thinking, not as definitive truth. Claims are verified against publicly available sources. Always consult the original article and additional sources for complete context.