ReviewNature communications2025
Artificial intelligence for modeling and understanding extreme weather and climate events.
Review in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predictors of rural hospital closures in the United States: a systematic review and call for AI-driven early warning systems.BMC health services research · 2025Pooled it
- Predicting regional gray swans via translocation: AI weather models and Dubai's unprecedented 2024 rainfall.Science advances · 2026Article
- Dynamics-informed machine learning for recovering extensive missing systems dynamics.Nature communications · 2026Article
- Forecasting extreme temperature events by Hankel-augmented contrastive learning.National science review · 2026Article
- Article
- Bridging the weather and climate divide with artificial intelligence.Nature communications · 2026Review
- Explainable AI: learning from the learners.Nature communications · 2026Review
- Patterns, drivers, and trends of urban cooling demand across global cities.Nature communications · 2026Article
- Transforming global water cycle observations via synergistic AI and remote sensing.Science advances · 2026Article
- Article
- AI-driven personalization of food systems: From precision nutrition to climate-resilient supply chains.NPJ science of food · 2026Review
- Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia.Scientific reports · 2026Article
- A Community Dataset for Large-Scale River Nitrogen Modeling in the United States.Scientific data · 2026Article
- District-Level Dengue Early Warning Prediction System in Bangladesh Using Hybrid Explainable AI and Bayesian Deep Learning.Tropical medicine and infectious disease · 2026Article
- How AI Shapes the Future Landscape of Sustainable Building Design With Climate Change Challenges?Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Interpretable machine learning for shoreline forecasting.Scientific reports · 2026Article
- Agentic AI can help hospitals prepare for unprecedented weather.NPJ digital medicine · 2026Article
- Unlocking the benefits of transparent and reusable science for climate risk management.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations.NPJ climate and atmospheric science · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
25 authors.
Funding
Abstract
In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences, by improving weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. The latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous, small sample sizes and data with limited annotations. This paper reviews how AI is being used to analyze extreme climate events (like floods, droughts, wildfires, and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating real-time information, and deploying understandable models, all crucial steps for gaining stakeholder trust and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy to enhance disaster readiness and risk reduction.
Identifiers
What OpenQuestion holds
Registered trials
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.