"Demystifying the climate black box." Join us in mapping the future of explainable AI and weather foundation models.
Climate change represents an existential race that humanity cannot afford to lose. Through accurate weather forecasting models and Environmental Sciences, researchers make informed choices towards a more sustainable future. While traditional models require immense computational resources, Machine Learning (ML) has proven to be a viable and efficient solution as more data is collected. Weather and Climate forecasting Foundation Models (FM) have shown that comparable results can be achieved with a fraction of the resources, opening the field to smaller countries and research teams with less investment and representation. While this provided a solution to the "efficiency" crisis in climate modeling, it introduced a "transparency" crisis: these black-box models pose a significant threat to scientific trust and social equity, impeding wider adoption in extreme weather event preparation and intervention, and in sustainable initiatives and global net-zero projects.
This workshop focuses on two converging crises: the technical challenge of extracting physically consistent explanations from high-dimensional FMs, and the risk that non-transparent models will reinforce existing inequalities through biased resource allocation. By integrating eXplainable Artificial Intelligence (XAI) into the modeling pipeline, we aim to reduce the interpretability gap and bias of FMs. By bringing together ML researchers, climate scientists, and policy experts, we investigate the fundamental role of interpretable architectures in addressing extreme weather events and building a low-carbon society for a better future.
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