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AI is helping build Destination Earth (DestinE), a European Union initiative that aims to create a digital replica of the Earth system by 2030. It combines Earth-system simulations, observations, data infrastructure and high-performance computing; machine learning is being developed to complement the physics-based models, strengthen uncertainty analysis and make the system more useful to explore. It is not yet a complete, all-purpose real-time copy of the planet.
What Destination Earth’s digital twin is
DestinE is a public European initiative, not a single commercial AI product. Its digital twin is a simulation framework grounded in Earth-system models and data—not simply a three-dimensional globe. The first two twins focus on climate-change adaptation and weather-induced extreme events. They are designed to support high-resolution simulations and tailored scenarios at global and European scales. ECMWF’s Destination Earth overview describes the initiative and its infrastructure.
Climate-change adaptation
The Climate Change Adaptation Digital Twin supports global, high-resolution, multi-decadal climate projections. Users can explore tailored what-if scenarios to examine how climate conditions and impacts may change.
Weather-induced extremes
The Weather-Induced Extremes Digital Twin provides a flexible framework for simulating extreme events and their associated impacts. Its scope includes global and European domains, with high-resolution modeling intended to help institutions investigate how events unfold.
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How AI fits alongside Earth-system simulation
AI is being built into the modeling and data workflow as a complement to Earth-system simulation, not as a replacement for observations or physical models. ECMWF’s AI in DestinE program sets out four aims:
- Develop machine-learning components toward a European ML-based Earth-system model.
- Adapt the Digital Twin Engine so it can support model training and operation on EuroHPC supercomputers.
- Apply AI to uncertainty quantification, helping users assess the limits and spread of model results.
- Improve how users interact with and make use of twin data.
As of ECMWF’s 30 June 2026 update, AI components had been developed for land, ocean, sea ice, waves and hydrology. The next phase aims to couple components for different parts of the Earth system and produce high-quality, AI-ready datasets for Europe’s AI Factories. These are development goals; the update does not establish that all components are already integrated or that downstream applications have delivered measured benefits. ECMWF’s phase-three update describes that work.
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How the AI work relates to forecasting
ECMWF also identifies Anemoi, an open-source machine-learning framework, and its operational Artificial Intelligence Forecasting System (AIFS) as part of the broader AI context. AIFS is not the complete DestinE digital twin: the twin includes its own Earth-system simulation, data and computing infrastructure.
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DestinE is intended to help European and national institutions explore past, present and likely future climate conditions and extreme events, and to test tailored scenarios. For example, a simulation can replay a record-breaking heatwave or devastating flood and examine how it might unfold in a different climate, such as a world 2°C warmer. That is a scenario exercise, not a prediction that a specific future event will occur with certainty.
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The twins are designed to complement existing European weather and climate prediction capabilities. In phase three, the work includes operating and evolving the twins and Digital Twin Engine, evaluating results, quantifying uncertainty, and linking the twins more closely to produce physically consistent storylines across scales. ECMWF describes these operational priorities.
How the initiative is organized and powered
Different European organizations deliver distinct parts of DestinE. The European Commission’s Directorate-General for Communications Networks, Content and Technology (DG CNECT) leads the initiative. ECMWF is responsible for the Earth-System Digital Twins and Digital Twin Engine; the European Space Agency (ESA) delivers the Core Service Platform; and EUMETSAT delivers the Data Lake. The Data Lake is intended to bring together existing European holdings and new twin data, including sources such as Copernicus, implementing entities’ holdings, and other data such as Internet of Things and socioeconomic data. The architecture is designed to support access and processing close to the data. The official overview sets out these roles.
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EuroHPC supercomputers provide the computing infrastructure for high-resolution Earth-system modeling and Digital Twin Engine workflows. ECMWF names LUMI, MareNostrum 5 and Leonardo among the systems on which its twins are deployed. A 5 km resolution figure published by ECMWF in February 2026 refers specifically to an example global simulation visualizing cloud systems; it should not be treated as the resolution of every DestinE product. ECMWF’s phase-three announcement provides that example.
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Where the project stands and what comes next
The initiative’s stated goal is a digital replica of the planet by 2030. ECMWF confirmed phase three on 1 February 2026; it runs from June 2026 through June 2028. The phase focuses on operating and evolving the twins and Engine, advancing AI models, and creating AI-ready datasets. In its 30 June 2026 update, ECMWF said the first two phases had matured and operationalised the twins and Engine, with workflows, evaluation, uncertainty quantification, data governance and user access consolidated. More than 100 partner organisations took part across those first two phases, according to ECMWF. The phase announcement and the June update describe the timeline and progress.
What the digital twin can—and cannot—claim
- It can: provide a framework for exploring high-resolution climate projections, extreme-event simulations and tailored scenarios, with AI components intended to support modeling, uncertainty analysis and data use.
- It is not established as: a complete real-time copy of Earth, a system that predicts any particular future extreme event with certainty, or a proven accuracy improvement over other forecast or climate systems.
- Its limits matter: the official project accounts do not provide an independent comparative benchmark or accuracy percentage. A higher spatial resolution is not, by itself, evidence of greater predictive accuracy.
These distinctions are important when interpreting project announcements. The initiative’s stated direction is an integrated AI-enabled Earth-system model, but its components, user services and datasets are being developed and operated in phases rather than delivered as one finished planetary replica.
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