@article{reichstein2025early,
    type = {article},
    key = {reichstein2025early},
    author = {Markus Reichstein and Vitus Benson and Jan Blunk and Gustau Camps-Valls and Felix Creutzig and Carina J. Fearnley and Boran Han and Kai Kornhuber and Nasim Rahaman and Bernhard Schölkopf and José María Tárraga and Ricardo Vinuesa and Karen Dall and Joachim Denzler and Dorothea Frank and Giulia Martini and Naomi Nganga and Danielle C. Maddix and Kommy Weldemariam},
    title = {Early Warning of Complex Climate Risk with Integrated Artificial Intelligence},
    journal = {Nature Communications},
    volume = {16},
    number = {1},
    year = {2025},
    month = {mar},
    pages = {},
    doi = {10.1038/s41467-025-57640-w},
    url = {https://doi.org/10.1038/s41467-025-57640-w},
    abstract = {As climate change accelerates, human societies face growing exposure to disasters and stress, highlighting the urgent need for effective early warning systems (EWS). These systems monitor, assess, and communicate risks to support resilience and sustainable development, but challenges remain in hazard forecasting, risk communication, and decision-making. This perspective explores the transformative potential of integrated Artificial Intelligence (AI) modeling. We highlight the role of AI in developing multi-hazard EWSs that integrate Meteorological and Geospatial foundation models (FMs) for impact prediction. A user-centric approach with intuitive interfaces and community feedback is emphasized to improve crisis management. To address climate risk complexity, we advocate for causal AI models to avoid spurious predictions and stress the need for responsible AI practices. We highlight the FATES (Fairness, Accountability, Transparency, Ethics, and Sustainability) principles as essential for equitable and trustworthy AI-based Early Warning Systems for all. We further advocate for decadal EWSs, leveraging climate ensembles and generative methods to enable long-term, spatially resolved forecasts for proactive climate adaptation.},
}