Join us in mapping the future of trustworthy AI Foundation Models for Weather and Climate
Accurate weather and climate forecasting models enable researchers and policymakers to make informed decisions towards a more sustainable future. However, traditional numerical models demand immense computational resources. Machine Learning (ML) has emerged as a viable and efficient alternative, benefiting from the ever-growing volume of collected data. In particular, weather and climate Foundation Models (FMs) achieve comparable results at a fraction of the computational cost, opening the field to smaller countries and research teams with limited resources and representation. Yet while FMs partly address computational costs issues, they introduce a transparency one. Unlike physics-based models, whose predictions can be traced to known equations with a relatively well understood physics, FMs results may prove difficult to understand, especially when they produce physical hallucinations. This opacity may undermine scientific trust and raise equity concerns.
Explainable Artificial Intelligence (XAI) offers a promising path to address this challenge. By revealing which inputs and internal mechanisms drive a model's predictions, XAI methods can help verify that FMs rely on physically meaningful patterns rather than spurious correlations, detect and diagnose physical hallucinations, and communicate the rationale behind forecasts to scientists and decision makers alike. In doing so, XAI can restore the scientific scrutiny that black-box models currently lack, fostering trust in ML-based forecasting and supporting its equitable adoption across the globe. In this workshop we focus on the issues just introduced, by bringing together ML researchers, climate scientists, and policy experts.
By bringing together ML researchers, climate scientists, and policy experts, the program focuses on three threads:
Tentative — one day, four invited talks, a panel, and two poster sessions.
| Time | Session |
|---|---|
| 8:00 – 8:15 am | Opening Remarks |
| 8:15 – 9:00 am | Invited talk I Gustau Camps-Valls Universitat de València |
| 9:00 – 9:45 am | Invited talk II Megan J. Stanley Ellison Institute of Technology Oxford |
| 9:45 – 10:30 am | Invited talk III Lily Xu Columbia University |
| 10:30 am – 12:00 pm | BreakPoster Session I & Coffee Break |
| 12:00 – 1:00 pm | Lunch Break |
| 1:00 – 2:00 pm | Contributed Talks (I, II, III, and IV) |
| 2:00 – 2:45 pm | Invited talk IV David Rolnick McGill University & Mila |
| 2:45 – 4:15 pm | BreakPoster Session II & Coffee Break |
| 4:15 – 5:00 pm | (Panel) The Interpretability Gap: Is a "Black Box" Prediction Better than No Prediction? |
(1) Regular Paper Track: up to 8 pages, excluding references and appendices.
(2) Tiny Paper Track: up to 5 pages, excluding references and appendices.
Unlimited pages are allowed for references and appendices in the same PDF as the main paper.
Submissions must be in a single PDF file and are required to use the NeurIPS 2026 LaTeX template, available on the NeurIPS 2026 Main Track Handbook.
All submissions must be made via OpenReview.
Please make sure that all authors have an OpenReview profile with the latest information. Creating one may take up to 2 weeks.
We welcome optional anonymous submissions of ongoing and unpublished work on any topics related to the workshop.
We require each submission to nominate at least one author to serve as a reviewer, following the NeurIPS 2026 reciprocal review rule, and we aim for each paper to collect at least three reviews.
Contributed talks' speakers will be selected from the top submissions received, giving deserving works a spotlight to a broad audience. Each speaker will have 15 minutes (10+5) for presentation and Q&A.
All dates are in AoE (Anywhere on Earth) time.
| Submission Deadline | August 29, 2026, AoE |
| Review Period | September 1 – September 15, 2026, AoE |
| Rebuttal Period Ends | September 20, 2026, AoE |
| Advisory Committee Discussion Ends | September 27, 2026, AoE |
| Notification of Acceptance | September 28, 2026, AoE |
| Workshop Date | NeurIPS 2026 (TBD) |
National University of Singapore
National University of Singapore
University of Amsterdam
National University of Singapore