Rising temperatures are driving more frequent and intense extreme weather—catastrophic floods, powerful hurricanes, cyclones, and drought‑exacerbated wildfires. Yet the tools used by local communities, emergency agencies, insurers, and risk markets have lagged behind the latest data and models, limiting accurate predictions of event evolution. To close this gap, MIT launched one of its 2022 Climate Grand Challenges, titled Preparing for a New World of Weather and Climate Extremes, focusing on assessing a location’s vulnerability to floods, cyclones, humid heat waves, and other climate hazards. Four years on, over 40 faculty and students have produced 29 papers and a suite of digital tools and datasets that are already deployed or near deployment, spanning forecasting, risk assessment, on‑ground planning, and resilient infrastructure.\ \ Reducing Scientific Uncertainty\ Paul O’Gorman, Robert R. Shrock Professor of Earth and Planetary Sciences, is refining the science behind extreme‑event forecasts, studying events such as the 2023 Central Texas and Pakistan floods. His team discovered that seasonal shifts in extreme rainfall are driven not only by specific humidity (the amount of water vapor) but also by how close the air is to saturation, measured by relative humidity. Relative humidity depends on air circulation, land‑sea temperature contrast, soil moisture, and vegetation, creating a complex story that improves our understanding of precipitation patterns.\ \ Hurricane and Severe Convective Storm Risk\ Kerry Emanuel, professor of atmospheric science, notes that coarse‑grained weather data can reproduce real‑world hurricane statistics, but risk estimation for severe convective storms—thunderstorms and tornadoes—remains immature. These storms have caused more deaths and damage than hurricanes in the past decade. Emanuel’s methods are already used by First Street to provide environmental risk scores for every private property in the United States.\ \ Improving Community Resilience\ Miho Mazereeuw, associate professor of architecture, translates scientific models into tools for planners. Working with Boston and Broward County, Florida, her team built interactive web‑based applications that let users explore flood impacts across many scenarios and instantly answer practical questions, such as which schools remain driest under different flood conditions. Her Urban Risk Lab bridges the gap between scientific knowledge and actionable public information through community emergency data collection, proactive recovery planning, and AI‑assisted large‑scale climate‑impact visualizations.\ \ Optimizing Energy Infrastructure\ Michael Howland, Jeffrey Cheah Career Development Professor of Civil and Environmental Engineering, leads a team—including Jessika Trancik from the Institute of Data Systems and Society and Moshe Ben‑Akiva from Civil Engineering—to analyze extreme‑weather impacts on the power grid and develop optimization models for siting and sizing new energy assets. Their model placed wind farms along the Gulf Coast of Texas, a counter‑intuitive choice given lower average wind speeds there. However, the daily wind cycle there complements solar generation, smoothing overall output and reducing storage and backup needs. Ongoing work aligns generation with time‑ and space‑varying electricity demand to further cut storage, transmission, and backup requirements, aiming to deliver actionable siting and sizing guidance to grid planners.\ \ Cross‑Disciplinary Decision Support\ Emanuel adds that turning detailed risk information into political, economic, and civic decisions is slow—"like steering a supertanker"—but as models become more realistic and tools mature, communities will gain stronger early‑warning, preparation, and recovery capabilities.\ \ Review