As climate change intensifies, our ability to predict and respond to cascading and compounding disasters grows increasingly critical. Floods, droughts, wildfires, and extreme storms are no longer isolated events; they interact in ways that defy traditional prediction systems.
One way to address this challenge is to leverage artificial intelligence (AI) to create integrated, impact-focused early-warning systems (EWS). Researchers, including our team at Amazon Web Services, are exploring how AI can transform EWS into more localized, impact-driven, and accessible systems.
In our recent perspective, "Early warning of complex climate risk with integrated artificial intelligence", in Nature Communications, we explore how advances in AI can redefine disaster preparedness, making it more actionable, inclusive, and effective.
The complexity of climate risks
The same meteorological event — a severe storm, for instance — can devastate one region while sparing another. This variability stems from differences in geography, infrastructure, and social vulnerability. Furthermore, risks increasingly cascade: droughts can lead to wildfires, which in turn affect air quality and public health. As evidenced by recent events — including, but not limited to, the L.A. wildfires — these risks affect not only low-income countries and regions but even the richest communities. Traditional EWS struggle to account for these intricate interactions, often focusing narrowly on predicting hazards themselves rather than their broader impacts.
A paradigm shift with AI
AI-driven systems are uniquely capable of addressing these challenges by integrating data across domains — meteorological, geospatial, and socioeconomic — and making sense of their interplay. Here’s how integrated AI is pushing the boundaries of what’s possible:
- Hazard-to-impact forecasting: Traditional EWS stop at weather predictions, leaving users to infer impacts. AI allows us to directly model the effects of hazards, such as predicting the likelihood that a flood will disrupt transportation or a heatwave will cause food insecurity. This shift toward impact-based forecasting turns data into actionable insights.
- Localized and personalized warnings: By combining high-resolution satellite imagery with localized socioeconomic data, AI systems can tailor warnings to specific communities and even individual users. For instance, an AI-driven system might alert urban residents about potential flooding in specific neighborhoods while advising rural farmers on crop protection.
- Faster and smarter predictions: Modern AI models, such as foundation models for meteorology, process vast datasets more efficiently than traditional numerical models. Modern models can provide faster, high-resolution forecasts, offering increased lead times that can mean the difference between mitigation and disaster.
- Decentralization: In line with Amazon chief technology officer Werner Vogels’s 2025 tech predictions blog, open data and decentralized approaches can empower local communities to take ownership of disaster preparedness. Complementing important centralized infrastructures, AI-driven systems that leverage open-source models and publicly available data can ensure that even resource-constrained regions can deploy high-quality EWS. Decentralization not only democratizes access but fosters resilience by enabling regions to adapt systems to their unique needs.
Responsible AI for equitable outcomes
The promise of AI comes with responsibility. As we adopt these technologies, adhering to principles of fairness, accountability, transparency, ethics, and sustainability (FATES) is essential. Those principles include
- avoiding biases: AI models trained on data from the Global North must be adapted to diverse contexts, ensuring equitable performance worldwide;
- transparency: clear communication of AI predictions, including their uncertainties, helps users make informed decisions;
- data ownership: as Vogels noted, decentralized systems thrive when they are built with inclusivity and local engagement; empowering local communities to contribute to and govern the data used in EWS not only fosters trust but ensures relevance.
Toward the next generation of EWS
The integration of AI into EWS isn’t only about better predictions; it’s about the whole early-warning chain and preparedness. Whether it’s urban planners designing resilient infrastructure, farmers adapting to seasonal forecasts, or humanitarian agencies implementing anticipatory action, AI transforms how we prepare for and respond to risks.
Again consistent with Vogels’s view on disaster preparedness, the future of EWS must embrace modularity and interconnectivity. AI-enabled EWS should not rely on one-size-fits-all solutions. Instead, flexible, modular systems that can integrate seamlessly into local contexts will be essential for empowering communities to act independently while benefiting from global innovations.
Looking ahead, the next frontier is to combine multihazard, multiscale foundation models that can seamlessly integrate meteorological, geospatial, and socioeconomic data with physically based models in hybrid approaches that promise better interpretability and scientific consistency. These systems hold the potential to offer not just warnings but comprehensive scenarios, helping society navigate the uncertainty of a changing climate.
Imagine a future where your smartphone provides personalized alerts during extreme weather, combining global satellite data with hyperlocal insights; where farmers receive AI-driven advice on crop protection; and where urban planners use generative models to visualize the impact of floods on infrastructure. Innovations in AI and open data are helping make this possible.
As we enter this transformative era, collaboration between researchers, public institutions, and the private sector will be key. Together, we can help AI-enabled EWS to not only mitigate risks but also to provide a foundation for resilient and sustainable communities worldwide.
Acknowledgments: Danielle Robinson, Kommy Weldemariam
