Extreme weather events, such as heat waves, are likely to become more frequent and more intense within the next few years. In the United States, the frequency has increased steadily, from an average of two heat waves per year during the 1960s to about six per year during the 2010s.

These maps show changes in the number of heat waves per year (frequency); the average length of heat waves in days (duration); the number of days between the first and last heat wave of the year (season length); and how hot the heat waves were, compared with the local temperature threshold for defining a heat wave (intensity). These data were analyzed from 1961 to 2023 for 50 large metropolitan areas. The size of each circle indicates the rate of change per decade. Solid-color circles represent cities where the trend was statistically significant. For more information: www.epa.gov/climate-indicators

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

In the United States, the average heat wave has been about four days long in the 2010s. This is about a day longer than the average heat wave that was experienced in the 1960s.

In the United States, the frequency has increased steadily, from an average of two heat waves per year to six per year between 1960s and the 2010s.

Extreme weather events, including fires, heatwaves, and droughts, have significant impacts on earth, environmental, and energy systems. Mechanistic and predictive understanding, as well as probabilistic risk assessment of these extreme weather events, are crucial for detecting, planning for, and responding to these extremes. Records of extreme weather events provide an important data source for understanding present and future extremes, but the existing data needs preprocessing before it can be used for analysis. Moreover, there are many nonstandard metrics defining the levels of severity or impacts of extremes. In this study, we compile a comprehensive benchmark data inventory of extreme weather events, including fires, heatwaves, and droughts. The dataset covers the period from 2001 to 2020 with a daily temporal resolution and a spatial resolution of 0.5°×0.5° (~55km×55km) over the continental United States (CONUS), and a spatial resolution of 1km × 1km over the Pacific Northwest (PNW) region, together with the co-located and relevant meteorological variables. By exploring and summarizing the spatial and temporal patterns of these extremes in various forms of marginal, conditional, and joint probability distributions, we gain a better understanding of the characteristics of climate extremes. The resulting AI/ML-ready data products can be readily applied to ML-based research, fostering and encouraging AI/ML research in the field of extreme weather. This study can contribute significantly to the advancement of extreme weather research, aiding researchers, policymakers, and practitioners in developing improved preparedness and response strategies to protect communities and ecosystems from the adverse impacts of extreme weather events. Usage Notes We presented a long term (2001-2020) and comprehensive data inventory of historical extreme events with daily temporal resolution covering the separate spatial extents of CONUS (0.5°×0.5°) and PNW(1km×1km) for various applications and studies. The dataset with 0.5°×0.5° resolution for CONUS can be used to help build more accurate climate models for the entire CONUS, which can help in understanding long-term climate trends, including changes in the frequency and intensity of extreme events, predicting future extreme events as well as understanding the implications of extreme events on society and the environment. The data can also be applied for risk accessment of the extremes. For example, ML/AI models can be developed to predict wildfire risk or forecast HWs by analyzing historical weather data, and past fires or heateave , allowing for early warnings and risk mitigation strategies. Using this dataset, AI-driven risk assessment models can also be built to identify vulnerable energy and utilities infrastructure, imrpove grid resilience and suggest adaptations to withstand extreme weather events. The high-resolution 1km×1km dataset ove PNW are advantageous for real-time, localized and detailed applications. It can enhance the accuracy of early warning systems for extreme weather events, helping authorities and communities prepare for and respond to disasters more effectively. For example, ML models can be developed to provide localized HW predictions for specific neighborhoods or cities, enabling residents and local emergency services to take targeted actions; the assessment of drought severity in specific communities or watersheds within the PNW can help local authorities manage water resources more effectively.

BackgroundHeatwaves have been linked to increased risk of mortality and morbidity and are projected to increase in frequency and intensity due to climate change. The current study uses emergency department (ED) data from Australia, Botswana, Netherlands, Pakistan, and the United States of America to evaluate the impact of heatwaves on ED attendances, admissions and mortality.MethodsRoutinely collected time series data were obtained from 18 hospitals. Two separate thresholds (≥4 and ≥7) of the acclimatisation excess heat index (EHIaccl) were used to define “hot days”. Analyses included descriptive statistics, independent samples T-tests to determine differences in case mix between hot days and other days, and threshold regression to determine which temperature thresholds correspond to large increases in ED attendances.FindingsIn all regions, increases in temperature that did not coincide with time to acclimatise resulted in increases in ED attendances, and the EHIaccl performed in a similar manner. During hot days in California and The Netherlands, significantly more children ended up in the ED, while in Pakistan more elderly people attended. Hot days were associated with more patient admissions in the ages 5–11 in California, 65–74 in Karachi, and 75–84 in The Hague. During hot days in The Hague, patients with psychiatric symptoms were more likely to die. The current study did not identify a threshold temperature associated with particularly large increases in ED demand.InterpretationThe association between heat and ED demand differs between regions. A limitation of the current study is that it does not consider delayed effects or influences of other environmental factors. Given the association between heat and ED use, hospitals and governmental authorities should recognise the demands that heat can place on local health care systems. These demands differ substantially between regions, with Pakistan being the most heavily affected within our study sample.

Climate change has heightened the need to understand physical climate risks, such as the increasing frequency and severity of heat waves, for informed financial decision-making. This study investigates the financial implications of extreme heat waves on stock returns in Europe and the United States. Accordingly, the study combines meteorological and stock market data by integrating methodologies from both climate science and finance. The authors use meteorological data to ascertain the five strongest heat waves since 1979 in Europe and the United States, respectively, and event study analyses to capture their effects on stock prices across firms with varying levels of environmental performance. The findings reveal a marked increase in the frequency of heat waves in the 21st century, reflecting global warming trends, and that European heat waves generally have a higher intensity and longer duration than those in the United States. This study provides evidence that extreme heat waves reduce stock values in both regions, with portfolio declines of up to 3.1%. However, there are marked transnational differences in investor reactions. Stocks listed in the United States appear more affected by the most recent heat waves compared to those further in the past, whereas the effect on European stock prices is more closely tied to event intensity and duration. For the United States sample only, the analysis reveals a mitigating effect of high corporate environmental performance against heat risk. This study introduces an innovative interdisciplinary methodology, merging meteorological precision with financial analytics to provide deeper insights into climate-related risks.

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

Historical weather data is essential for understanding environmental trends, assessing climate risk, and building predictive models for infrastructure, agriculture, and sustainability initiatives. Among all variables, temperature and humidity serve as core indicators of environmental change and operational risk.

Ambios offers high-resolution Historical Weather Data focused on temperature and humidity, sourced from over 3,000+ first-party sensors across 20 countries. This dataset provides hyperlocal, verified insights for data-driven decision-making across industries.

-Historical weather records for temperature and humidity -First-party sensor data from a decentralized network -Global coverage across 20 countries and diverse climate zones -Time-stamped, high-frequency measurements with environmental context -Designed to support ESG disclosures, research, risk modeling, and infrastructure planning

Use cases include:

-Long-term climate trend analysis and model validation -Historical baselining for ESG and sustainability frameworks -Resilience planning for heatwaves, humidity spikes, and changing climate conditions -Agricultural research and water management strategy -Infrastructure and energy load forecasting -Academic and scientific studies on regional weather patterns

Backed by Ambios’ decentralized physical infrastructure (DePIN), the data is reliable, traceable, and scalable—empowering organizations to make informed decisions grounded in historical environmental intelligence.

Whether you're building ESG models, planning smart infrastructure, or conducting climate research, Ambios Historical Weather Data offers the precision and credibility needed for long-term environmental insight.

The U.S. Climate Risk Projections by County, 2040-2049 data set contains a projection for 2040-2049 risk for the entire contiguous U.S. at the county level with a novel climate risk index integrating multiple hazards, exposures and vulnerabilities. Multiple hazards such as weather and climate are characterized as a frequency of heat wave, cold spells, drought, and heavy precipitation events along with anomalies of temperature and precipitation using high resolution (4 km) downscaled climate projections. Exposure is characterized by projections of population, infrastructure, and built surfaces prone to multiple hazards including sea level rise and storm surges. Vulnerability is characterized by projections of demographic groups most sensitive to climate hazards. This approach can guide planners in targeting counties at most risk and where adaptation strategies to reduce exposure or protect vulnerable populations might be best applied.

Heatwave Demand Response Analytics Market Outlook

According to our latest research, the global Heatwave Demand Response Analytics market size reached USD 2.48 billion in 2024, with a robust year-on-year growth supported by the increasing frequency and intensity of heatwaves worldwide. The market is anticipated to expand at a CAGR of 13.7% during the forecast period, reaching approximately USD 7.43 billion by 2033. This remarkable growth is primarily driven by the urgent need for advanced analytics to manage grid stability and optimize energy consumption during extreme weather events, as well as regulatory mandates promoting sustainable energy practices and grid modernization.

The principal growth factor for the Heatwave Demand Response Analytics market is the escalating occurrence of severe heatwaves, which have placed unprecedented stress on global energy grids. As temperatures rise due to climate change, energy demand for cooling surges, often leading to grid overloads and blackouts. Utilities and grid operators are increasingly adopting demand response analytics to anticipate surges, optimize load distribution, and prevent outages. This proactive approach not only ensures grid stability but also minimizes operational costs by enabling real-time decision-making and load-shedding strategies. The integration of advanced analytics and AI-driven forecasting tools has further enhanced the efficiency and responsiveness of demand response programs, making them indispensable for modern energy management.

Another significant driver is the rapid digital transformation occurring within the energy sector. The proliferation of smart meters, IoT devices, and advanced communication networks has created a vast data ecosystem, which, when leveraged through demand response analytics, provides actionable insights into consumption patterns and grid performance. Regulatory frameworks in regions such as North America and Europe are increasingly mandating the adoption of smart grid technologies and demand-side management solutions to meet sustainability goals and reduce carbon emissions. This regulatory push, combined with the economic benefits of demand response programs, is compelling utilities, commercial entities, and industrial players to invest in heatwave demand response analytics solutions.

Furthermore, the rising adoption of renewable energy sources, such as solar and wind, is contributing to grid volatility due to their intermittent nature. Demand response analytics play a crucial role in balancing supply and demand by enabling dynamic load adjustments in response to fluctuations in renewable generation. The convergence of distributed energy resources, energy storage systems, and demand response analytics is creating a more resilient and flexible grid infrastructure. This ecosystem not only supports grid operators in managing peak loads during heatwaves but also empowers end-users to participate in demand response programs and benefit from dynamic pricing models, driving further market expansion.

From a regional perspective, North America continues to dominate the global Heatwave Demand Response Analytics market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, has been at the forefront of adopting demand response technologies, supported by robust regulatory frameworks and significant investments in smart grid infrastructure. Meanwhile, the Asia Pacific region is poised for the fastest growth, driven by rapid urbanization, increasing energy consumption, and government initiatives aimed at enhancing grid reliability. Europe remains a key market, leveraging stringent environmental policies and aggressive renewable energy targets to fuel demand for advanced analytics solutions. The Middle East & Africa and Latin America are also experiencing steady growth, albeit from a smaller base, as they focus on grid modernization and resilience to extreme weather events.

Component Analysis

The Heatwave Demand Response Analytics market by c

National Risk Index Version: March 2023 (1.19.0)A Heat Wave is a period of abnormally and uncomfortably hot and unusually humid weather typically lasting two or more days with temperatures outside the historical averages for a given area. Annualized frequency values for Heat Waves are in units of event-days per year.The National Risk Index is a dataset and online tool that helps to illustrate the communities most at risk for 18 natural hazards across the United States and territories: Avalanche, Coastal Flooding, Cold Wave, Drought, Earthquake, Hail, Heat Wave, Hurricane, Ice Storm, Landslide, Lightning, Riverine Flooding, Strong Wind, Tornado, Tsunami, Volcanic Activity, Wildfire, and Winter Weather. The National Risk Index provides Risk Index values, scores and ratings based on data for Expected Annual Loss due to natural hazards, Social Vulnerability, and Community Resilience. Separate values, scores and ratings are also provided for Expected Annual Loss, Social Vulnerability, and Community Resilience. For the Risk Index and Expected Annual Loss, values, scores and ratings can be viewed as a composite score for all hazards or individually for each of the 18 hazard types.Sources for Expected Annual Loss data include: Alaska Department of Natural Resources, Arizona State University’s (ASU) Center for Emergency Management and Homeland Security (CEMHS), California Department of Conservation, California Office of Emergency Services California Geological Survey, Colorado Avalanche Information Center, CoreLogic’s Flood Services, Federal Emergency Management Agency (FEMA) National Flood Insurance Program, Humanitarian Data Exchange (HDX), Iowa State University's Iowa Environmental Mesonet, Multi-Resolution Land Characteristics (MLRC) Consortium, National Aeronautics and Space Administration’s (NASA) Cooperative Open Online Landslide Repository (COOLR), National Earthquake Hazards Reduction Program (NEHRP), National Oceanic and Atmospheric Administration’s National Centers for Environmental Information (NCEI), National Oceanic and Atmospheric Administration's National Hurricane Center, National Oceanic and Atmospheric Administration's National Weather Service (NWS), National Oceanic and Atmospheric Administration's Office for Coastal Management, National Oceanic and Atmospheric Administration's National Geophysical Data Center, National Oceanic and Atmospheric Administration's Storm Prediction Center, Oregon Department of Geology and Mineral Industries, Pacific Islands Ocean Observing System, Puerto Rico Seismic Network, Smithsonian Institution's Global Volcanism Program, State of Hawaii’s Office of Planning’s Statewide GIS Program, U.S. Army Corps of Engineers’ Cold Regions Research and Engineering Laboratory (CRREL), U.S. Census Bureau, U.S. Department of Agriculture's (USDA) National Agricultural Statistics Service (NASS), U.S. Forest Service's Fire Modeling Institute's Missoula Fire Sciences Lab, U.S. Forest Service's National Avalanche Center (NAC), U.S. Geological Survey (USGS), U.S. Geological Survey's Landslide Hazards Program, United Nations Office for Disaster Risk Reduction (UNDRR), University of Alaska – Fairbanks' Alaska Earthquake Center, University of Nebraska-Lincoln's National Drought Mitigation Center (NDMC), University of Southern California's Tsunami Research Center, and Washington State Department of Natural Resources.Data for Social Vulnerability are provided by the Centers for Disease Control (CDC) Agency for Toxic Substances and Disease Registry (ATSDR) Social Vulnerability Index, and data for Community Resilience are provided by University of South Carolina's Hazards and Vulnerability Research Institute’s (HVRI) 2020 Baseline Resilience Indicators for Communities.The source of the boundaries for counties and Census tracts are based on the U.S. Census Bureau’s 2021 TIGER/Line shapefiles. Building value and population exposures for communities are based on FEMA’s Hazus 6.0. Agriculture values are based on the USDA 2017 Census of Agriculture.

In the last 40 years, trends in heat wave frequency, intensity, and duration have increased steadily around the world. These intense heat waves were characterized persistent atmospheric blocking episode, with a continuous presence of a warm air mass and lack of rain for several consecutive days, that contributed to pronounced positive temperature anomalies, reinforced by extremely low soil moisture, and warm and drought conditions. The year 2023 was the warmest year on record, and the global average temperature was +1.45°C above pre-industrial (1850–1900) values worldwide. In South America 2023 was the warmest since 1900, with 0.81°C above the 1991–2020 reference period. Central South America experienced a sequence of heatwaves series being the most intense during the autumn and spring of 2023. From August to December 2023, the meteorological services of Brazil, Argentina, Paraguay and Bolivia reported record-high maximum temperatures in this period in several stations east of the Andes and identified 7 heat waves episodes that affected all these countries. The large-scale circulation patterns show that heatwaves were characterized by an anomalously high-pressure system that facilitated the formation of a heat dome through dry, hot air columns over a warm and dry soil. Several locations experienced temperature of about 10°C above normal, and some locations reported maximum temperatures above 40°C for several days in a row. These heat waves aggravated the drought over Amazonia during the second half of 2023, during an El Niño year. Compound drought-heat favored hydrological drought, while the increased dryness amplified the risk of fires.

Led by the Massachusetts Executive Office of Energy and Environmental Affairs (EEA), in partnership with Cornell University, U.S. Geological Survey and Tufts University, the Massachusetts Climate and Hydrologic Risk Project (Phase 1) has developed new climate change projections for the Commonwealth. These new temperature and precipitation projections are downscaled for Massachusetts at the HUC8 watershed scale using Global Climate Models (GCMs) and a Stochastic Weather Generator (SWG) developed by Cornell University.

Stochastic weather generators provide a computationally efficient and complementary alternative to direct use of GCMs for investigating water system performance under climate stress. These models are configured based on existing meteorological records (i.e., historical weather) and are then used to generate large ensembles of simulated daily weather records that are similar to but not bound by variability in past observations. Once fit to historical data, model parameters can be systematically altered to produce new traces of weather that exhibit a wide range of change in their distributional characteristics, including the intensity and frequency of average and extreme precipitation, heatwaves, and cold spells.

The Phase 1 SWG was developed, calibrated, and validated across all HUC8 watersheds that intersect with the state of Massachusetts. A set of climate change scenarios for those watersheds were generated that only reflect mechanisms of thermodynamic climate change deemed to be most credible. These thermodynamic climate changes are based on the range of temperature projections produced by a set of downscaled GCMs for the region. The temperature and precipitation projections presented in this dashboard reflect a warming scenario linked to the Representation Concentration Pathway (RCP) 8.5, a comparatively high greenhouse gas emissions scenario.

The statistics presented in this series of map layers are expressed as either a percent change or absolute change (see list of layers with units and definitions below). These changes are referenced to baseline values that are calculated based on the median value across the 50 model ensemble members associated with the 0°C temperature change scenario derived from observational data (1950-2013) from Livneh et al. (2015). The temperature projections derived from the downscaled GCMs for the region, which are used to drive the SGW, are averaged across 30 years and centered on a target decade (i.e., 2030, 2050, 2070). Projections for 2090 are averaged across 20 years.Definitions of climate projection metrics (with units of change):Total Precipitation (% change): The average total precipitation within a calendar year. Maximum Precipitation (% change): The maximum daily precipitation in the entire record. Precipitation Depth – 90th Percentile Storm (% change): The 90th percentile of non-zero precipitation. Precipitation Depth –99th Percentile Storm (% change): The 99th percentile of non-zero precipitation. Consecutive Wet Days (# days): The average number of days that exist within a run of 2 or more wet days. Consecutive Dry Days (# days): The average number of days that exist within a model run of 2 or more dry days. Days above 1 inch (# days): The number of days with precipitation greater than 1 inch. Days above 2 inches (# days): The number of days with precipitation greater than 2 inches.Days above 4 inches (# days): The number of days with precipitation greater than 4 inches.Maximum Temperature (°F): The maximum daily average temperature value in the entire recordAverage Temperature (°F): Daily average temperature.Days below 0 °F (# days): The number of days with temperature below 0 °F.Days below 32 °F (# days): The number of days with temperature below 32 °F.Maximum Duration of Coldwaves (# days): Longest duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Average Duration of Coldwaves (# days): Average duration of coldwaves in the record, where coldwaves are defined as ten or more consecutive days below 20 °F.Number of Coldwave Events (# events): Number of instances with ten or more consecutive days with temperature below 20 °F.Number of Coldstress Events (# events): Number of instances when a 3-day moving average of temperature is less than 32 °F. Days above 100 °F (# days): The number of days with temperature above 100 °F.Days above 95 °F (# days): The number of days with temperature above 95 °F.Days above 90 °F (# days): The number of days with temperature above 90 °F.Maximum Duration of Heatwaves (# days): Longest duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Average Duration of Heatwaves (# days): Average duration of heatwaves in the record, where heatwaves are defined as three or more consecutive days over 90 °F.Number of Heatwave Events (# events): Number of instances with three or more consecutive days with temperature over 90 °F.Number of Heatstress Events (# events): Number of instances when a 3-day moving average of temperature is above 86 °F.Cooling Degree Days (# degree-day): Cooling degree days assume that when the outside temperature is below 65°F, we don't need cooling (air-conditioning) to be comfortable. Cooling degree-days are the difference between the daily temperature mean and 65°F. For example, if the temperature mean is 85°F, we subtract 65 from the mean and the result is 20 cooling degree-days for that day. (Definition adapted from National Weather Service).Heating Degree Days (# degree-day): Heating degree-days assume that when the outside temperature is above 65°F, we don't need heating to be comfortable. Heating degree days are the difference between the daily temperature mean and 65°F. For example, if the mean temperature mean is 25°F, we subtract the mean from 65 and the result is 40 heating degree-days for that day. (Definition adapted from National Weather Service).Growing Degree Days (# degree-day): A growing degree day (GDD) is an index used to express crop maturity. The index is computed by subtracting a base temperature of 50°F from the average of the maximum and minimum temperatures for the day. Minimum temperatures less than 50°F are set to 50, and maximum temperatures greater than 86°F are set to 86. These substitutions indicate that no appreciable growth is detected with temperatures lower than 50° or greater than 86°. (Adapted from National Weather Service).Please see additional information related to this project and dataset in the Climate Change Projection Dashboard on the Resilient MA Maps and Data Center webpage.

There is a general consensus that the global climate has slowly warmed (0.6 degrees C) over the past 100 years and that this trend will continue at an accelerated rate over the next 100 years (1 degree to 6 degrees C) (Kattenburg et al. 1996). Aside from the mean annual increase in temperature, the frequency of weather extremes, such as heat waves, drought, and tropical storms are projected to increase across North America over the next century (Easterling et al. 2000). Past changes in temperature and precipitation have been accompanied by changes in insect and vertebrate distributions from both tropical and temperate environments (Parmesan 1996, Pounds et. Al 1999). We have developed bird-habitat models that allow us to predict the occurrences of species in specific forest types within and across national forests within the Blue Ridge physiographic province with a high degree of accuracy (Linder et al. submitted). These models categorize habitat as unsuitable, marginal or high quality as determined by the rate of occupancy over five years of point count data. We will validate our occurrence models with reproductive data. We hypothesize that reproductive success will correspond to predicted habitat quality. While stochastic events may obscure patterns in the short-term, long-term reproductive trends should reflect site quality.

Shallow coastal ecosystems are threatened by marine heatwaves, but few long-term records exist to quantify these heatwaves. Here, 40-year records of measured water temperature were constructed for a site in a system of shallow bays with documented heatwave impacts and a nearby ocean site; available gridded sea-surface temperature datasets in the region were also examined. Water temperatures at both sites increased significantly though bay temperatures were consistently 3-4°C hotter in summer and colder in winter and were more variable overall, differences not captured in high-resolution gridded sea-surface temperature datasets. There was considerable overlap in heatwave events at the coastal bay and ocean sites. Annual heatwave exposure was similar and significantly increased at both sites while annual heatwave intensity was significantly higher at the bay site owing to the high variance of the daily temperature anomaly there. Event frequency at both sites increased at a rate of about 1 event/decade. Future simulations indicate all heatwave metrics increase, as do days above 28°C, a heat stress threshold for seagrass. Ocean temperatures on the U.S. mid-Atlantic margin have rarely exceeded this threshold, while summer bay temperatures commonly do, allowing ocean exchange with coastal bays to provide thermal relief to bay ecosystems. This will have changed by 2100, creating a thermal environment that threatens seagrass communities in these systems. Documenting such change requires development of long-term water temperature records in more shallow coastal systems.

IntroductionCoastal marine ecosystems, are particularly susceptible to climate change. One such threat is atmospheric heatwaves, which are predicted to increase in frequency, duration, and intensity. Many intertidal organisms already live at the edge of their thermal tolerance limits and heatwaves can outstretch an organism’s ability to compensate in the short term. In June 2021 the Pacific Northwest region of North America, including the Salish Sea, experienced a significant atmospheric heatwave during some of the lowest tides of the year. This was followed by numerous reports of dead and dying intertidal marine organisms region-wide. A semi-quantitative rapid assessment found a range of both species- and location-specific effects but generally recorded widespread negative impacts to intertidal shellfish species across the Salish Sea. MethodsFollowing these results, we opportunistically analyzed data collected by intertidal bivalve resource managers from the region. These datasets allowed us to examine regional density and size data for clam and oyster populations before and after the heatwave to increase our quantitative understanding of heatwave effects. ResultsWe found a range of responses including positive and negative effects of the heatwave on clam and oyster density. While we generally found small changes in bivalve size, some site-species combinations displayed large shifts in size frequency. Many of our analyses did not indicate even moderate statistical support, even with large changes in the mean, driven in part by high variability in the data. Time intervals between surveys, ranging from 2 to over 25 months, had little effect on observed variability indicating that any heatwave-induced effects may be masked by variability inherent to the population ecology and/or survey methodology. DiscussionThis analysis has highlighted the need for intertidal resource managers, and the greater research community, to consider alternative survey approaches designed to constrain variability in order to detect the effects of acute or extreme events. With the effects of climate change predicted to become more intense, targeted survey approaches may be needed to detect the effects and implications of such events and to continue effective management of intertidal bivalves in the Salish Sea and worldwide.

Climate-resilient Energy Efficiency Market Outlook

According to our latest research, the global climate-resilient energy efficiency market size reached USD 48.7 billion in 2024, driven by heightened concerns about climate change and the urgent need for sustainable infrastructure. The market is expected to grow at a robust CAGR of 12.8% from 2025 to 2033, with the market forecasted to reach USD 145.7 billion by 2033. This significant growth is underpinned by technological advancements, supportive regulatory frameworks, and increasing investments in energy-efficient solutions designed to withstand climate-related disruptions. As per the latest research, the momentum is further propelled by the growing adoption of renewable energy integration and the rising demand for resilient urban infrastructure globally.

The primary growth factor for the climate-resilient energy efficiency market is the intensifying impact of climate change, which is compelling governments, industries, and communities to prioritize energy solutions that not only reduce emissions but also enhance resilience against extreme weather events. The frequency and severity of climate-related disasters such as floods, hurricanes, and heatwaves have exposed the vulnerabilities in traditional energy systems, creating an urgent need for robust, adaptive, and efficient infrastructure. As a result, investments in technologies like smart grids, renewable energy integration, and advanced building automation are surging. These solutions not only ensure uninterrupted energy supply during climate emergencies but also contribute to long-term cost savings and environmental sustainability, making them increasingly attractive to both public and private stakeholders.

Another critical driver is the evolving regulatory landscape, with governments worldwide enacting stringent energy efficiency standards and offering incentives for climate-resilient infrastructure development. Policies such as the European Green Deal, the U.S. Infrastructure Investment and Jobs Act, and similar initiatives in Asia Pacific are catalyzing the adoption of advanced energy efficiency technologies. These regulations require organizations to invest in solutions that reduce carbon footprints and enhance operational resilience, thereby driving demand across residential, commercial, and industrial sectors. Furthermore, international collaborations and funding mechanisms, including those spearheaded by the United Nations and World Bank, are providing the necessary capital and technical expertise to accelerate market growth, particularly in emerging economies that are most vulnerable to climate change impacts.

Technological innovation is also playing a pivotal role in shaping the trajectory of the climate-resilient energy efficiency market. The proliferation of digital technologies, such as IoT-enabled building management systems, AI-driven energy analytics, and decentralized renewable energy platforms, is transforming how energy is produced, distributed, and consumed. These advancements enable real-time monitoring, predictive maintenance, and adaptive control, which are essential for maintaining energy efficiency and resilience under dynamic climate conditions. Moreover, the integration of energy storage solutions and microgrids is facilitating greater flexibility and reliability, ensuring that critical infrastructure remains operational during grid disruptions. As technology continues to evolve, the market is expected to witness the emergence of even more sophisticated solutions tailored to specific climate resilience needs.

From a regional perspective, North America and Europe currently dominate the climate-resilient energy efficiency market due to their advanced infrastructure, strong policy support, and high levels of investment in clean energy. However, the Asia Pacific region is emerging as a significant growth engine, fueled by rapid urbanization, increasing energy demand, and a heightened focus on climate adaptation strategies. Countries such as China, India, and Japan are making substantial investments in smart grids, renewable energy projects, and resilient urban planning. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with international aid and private sector participation driving the adoption of climate-resilient solutions. The diverse regional dynamics underscore the global imperative to build energy systems that can withstand the challenges posed by a changing climate.

Solution Analysis

The solution segment o

Percentage of respondents reporting experiencing overheating during heatwaves and a normal summer in the US and the UK.