Participants of the Berlin Summit on Earth Virtualization Engines (EVEs) discussed ideas and concepts to improve our ability to cope with climate change. EVEs aim to provide interactive and accessible climate simulations and data for a wide range of users. They combine high-resolution physics-based models with machine learning techniques to improve the fidelity, efficiency, and interpretability of climate projections. At their core, EVEs offer a federated data layer that enables simple and fast access to exabyte-sized climate data through simple interfaces. In this article, we summarize the technical challenges and opportunities for developing EVEs, and argue that they are essential for addressing the consequences of climate change. We are all witnessing the effects of climate change. Hotter summers, prolonged droughts, massive flooding, or ocean heat waves are examples of extreme weather and climate events that are growing in frequency and intensity. Many agree that addressing climate mitigation and adaptation is the biggest problem humanity faces today. A large group of scientists and practitioners from different climate-related domains, including some computer scientists, got together for a week in Berlin this July to discuss the concept of "Earth Virtualization Engines" (EVEs). The summit kicked off with the question: "If climate change is the most critical problem today, why are we not using the largest computers to help solve it?".