GeoHealth

Tropical cyclones are among the deadliest and costliest natural disasters in the United States, and the most intense storms are expected to become more frequent as the climate warms. Anticipating where deaths are most likely to occur is therefore central to preparedness, evacuation planning, and public health response. We modeled block-level mortality risk for twenty-four of the deadliest and costliest tropical cyclones to strike the U.S. Gulf and East Coasts, Puerto Rico, and the U.S. Virgin Islands between 1992 and 2024. For each storm, we combined NOAA hazard data (wind swaths, rainfall, and storm-surge inundation) with 2020 U.S. Census demographic and socioeconomic characteristics and the CDC/ATSDR Social Vulnerability Index for all Census blocks within 25 miles of the coast, and trained storm-specific boosted-tree models with population-standardized mortality as the outcome. Averaging block-level predictions within Saffir-Simpson categories yielded risk maps spanning tropical storms through Category 5 hurricanes. Predicted mortality risk rose with storm severity and concentrated in urban coastal communities of Puerto Rico, Louisiana, Florida, North Carolina, Virginia, Maryland, New Jersey, and New York, as well as in low-lying inlet, peninsula, and sound geographies. Large block population, non-Hispanic composition, male-dominated blocks, predominantly white blocks, and males aged 20 to 34 years ranked among the strongest predictors of mortality; patterns that likely reflect structural factors shaping exposure rather than individual susceptibility. The category-specific risk maps and an accompanying interactive dashboard provide a practical decision-support tool for emergency managers, planners, and coastal residents preparing for future storms.

BackgroundWest Nile virus (WNV), a mosquito-borne flavivirus, has circulated in the USA since 1999. In 2025, Florida was home to 24 million people, with projected increases in population and urbanization in a changing climate. The southern house mosquito, Culex quinquefasciatus, is found in every county, and is a major vector for WNV. Describing shifting WNV transmission risk is important to inform public health and vector control planning. Materials and MethodsUsing published estimates of thermal suitability of WNV transmission by Cx quinquefasciatus, with climate models and population data, we calculated and mapped baseline and projected county-level transmission suitability and people at risk (PAR) for 2000, 2030, and 2050. Five general circulation models (GCMs) and two mitigation scenarios (SSP2-4.5, SSP5-8.5) were used to explore future trajectories. ResultsAt baseline, all 67 counties in Florida experienced 5-9 months transmission suitability. Using the year 2000 census estimates, 2.33 million people in 2 counties experienced 9 months, and in 2030, across climate models, 8.93-12.26 million people (10-20 counties; SSP2-4.5), and 8.95-18.10 million people (11-26 counties; SSP5-8.5) are projected to experience 9 or more months of transmission suitability. In 2050, for both SSP2-4.5 and SSP5-8.5, 17.08-20.42 million people (23-26 counties), ranging to approximately 70% of the projected population of Florida will experience 9 or more months. The 10 most populated counties in 2000 are projected to experience 1-3 months of additional climate driven transmission suitability in the future. ConclusionThe southern house mosquito was previously managed as a seasonal nuisance in Florida but now represents an increasing public health exposure risk. Projections across climate trajectories underscore an increasing suitability and exposure risk for WNV in Florida, ranging as high as around 70% of the population exposed to suitable climate conditions for transmission for 9 or more months of the year in the 2050s. This means the types of operations and number of employees needed in vector control and public health will also increase.

BackgroundWest Nile virus (WNV) infection has caused over 30,000 human cases of the severe, neuroinvasive form of the disease (West Nile virus Neuroinvasive Disease; WNND) and nearly 3,000 deaths in the U.S. since its introduction in 1999. Despite spatiotemporal variation in the impact of WNV and its known links to various climate factors, no effective nationwide WNV or WNND forecast exists. ObjectivesWe aim to produce a skillful, nationwide WNND forecast built upon regionally varying relationships between climate factors and WNND. MethodsWe examined the impact of climate conditions on annual WNND caseload for 11 ecologically meaningful regions in the U.S. The most salient climate factors were incorporated into a regionally determined nationwide WNND forecast model. We retrospectively generated forecasts from 2005-2022 using observed climate conditions and compared forecast skill against various benchmarks, including a simple, historical case-driven model. Forecast skill was assessed by weighted interval scoring. ResultsRegional, climate-informed WNND retrospective forecasts outperformed a benchmark model only informed by historical WNND case data across all regions, as well as in a nationally aggregated score (univariate: 18.1% [3.7-27.0%], bivariate: 23.9% [9.0-32.8%]). Additionally, the regional forecasts outperformed an ensemble model generated from the 2022 CDC WNV Forecasting Challenge and a parallel, county-level, regional climate-informed forecast outperformed forecasts from the same Challenge. Drought and temperature were the climate factors most consistently linked to WNND and incorporated into our forecast model. DiscussionWe show a retrospectively generated WNND forecast for the continental U.S. that considerably improved upon simple forecasts based on historical case distributions. This forecast aggregated county-level data to broader regions to boost statistical signal and capture the regionally varying influences of climate conditions on annual WNND caseload. The advances here represent a potential path toward actionable broad-scale WNV forecasts.

Understanding how climate modulates infectious disease dynamics is critical for anticipating epidemic patterns. This study examines the association between climatological variables--specifically temperature and relative humidity--and the incidence of the SARS-CoV-2 Omicron variant (B.1.1.529) during its global wave (2021-2022). Using global epidemiological and climate data, we applied Scale-Dependent Correlation (SDC) analysis to detect transient, scale-specific associations across regions and periods. We identified consistent negative correlations between incidence and both temperature and humidity, especially in mid-latitudes during colder months. These findings were compared with predictions from stochastic population-based compartmental models incorporating climate-dependent transmission parameters. Among the tested formulations, the temperature-based model achieved the best fit to observed case trajectories. Our results highlight a robust climatological influence on Omicron transmission dynamics and underscore the importance of integrating climate indicators into epidemic modeling and preparedness strategies.

The COVID-19 pandemic had led to 500000 confirmed death by June 30, 2020. We combined the number of monthly confirmed new cases and deaths with latitude, temperature, humidity, rainfall, and sunshine ultraviolet (UV) to explore the climate impacts on COVID-19 fatality in 88 countries. There was a significant decrease in overall case-fatality rate in May and June (from 8.17% to 4.99% and 3.22%). The fatality in temperate marine regions was the highest (11.13%). The fatality was 5.71% in high latitudes ([&ge;]30{degrees}) but only 3.73% in low latitudes (<30{degrees}). The fatality was 6.76% in cold regions (<20{degrees}C) but only 3.90% in hot regions ([&ge;]20{degrees}C). The fatality was 5.87% in rainy regions ([&ge;]40mm) but only 3.33% in rainless regions (<40mm). The fatality was 6.57% in cloudy regions (<50) but only 3.86% in sunny regions ([&ge;]50). Traveling to hot sunny regions without pollution is a strategy for risk reduction.

Climate change has many adverse human health effects, including increased anxiety. However, eco-anxiety may also motivate climate action. An online survey was developed and distributed to examine factors associated with eco-anxiety. Logistic regression analysis showed that significant predictors of eco-anxiety include greater media exposure to climate change information, more frequent discussions about climate change with friends and family, the perception that climate change will soon impact one personally, being younger, and being female. Additional analyses suggested that ecoanxiety was associated with a range of both positive and negative emotional impacts including motivation, interest, sadness, and tension. Eco-anxiety was also associated with greater likelihood to engage in environmental behaviors such as recycling. Volunteering for environmental causes and accessing straightforward information with less scientific jargon were found to have particular potential for anxiety reduction among the eco-anxious. The research suggests practical strategies to reduce eco-anxiety while retaining engagement in mitigating climate change.

Seasonal temperature variation may impact the trajectories of COVID-19 in different global regions. Cumulative data reported by the World Health Organization, for dates up to March 27, 20201, show association between COVID-19 incidence and regions at or above 30{degrees} latitude. Historic climate data also show significant reduction of case rates with mean maximum temperature above approximately 22.5 degrees Celsius. Variance at the local level, however, could not be well explained by geography and temperature. These preliminary findings support continued countermeasures and study of SARS-CoV-2/COVID-19 transmission rates by temperature and humidity.

HighlightsO_LIIn March 2023, Cyclone Yaku was followed by a large dengue epidemic in northwest Peru C_LIO_LIExtreme precipitation during Cyclone Yaku caused 60% of dengue cases C_LIO_LIMore cyclone-attributable cases occurred in warm, urban, flood-susceptible districts C_LIO_LIGlobal warming has increased the risk of warm, very wet March weather in the region C_LI Science for SocietyAnthropogenic climate change is increasing the risk of extreme weather that can lead to infectious disease epidemics, but few studies have directly measured this health consequence of climate change. Extreme precipitation can drive outbreaks of mosquito-borne diseases by displacing people, disrupting public health activities, and creating aquatic breeding habitat. Here, we quantify the effects of extreme precipitation during Cyclone Yaku in northwestern Peru in March 2023. The cyclone was immediately followed by a dengue outbreak where cases exceeded historic averages by tenfold. We estimate that 60% of cases (or 22,014 cases) reported over three months in the affected districts were attributable to extreme precipitation during Cyclone Yaku. Compared with the preindustrial era, extremely wet March conditions were 31% more likely to occur (and 189% more likely to co-occur with warm temperatures suitable for dengue transmission) in recent decades in northwestern Peru. Assessing the linkages between climate change, extreme weather, and outbreaks of dengue and other infectious diseases is crucial for understanding the current impacts of climate change and for preparing for future health risks. eTOC SummaryThis study examines relationships between historical climate forcing, extreme weather, and health, focusing on Cyclone Yaku and Perus 2023 dengue epidemic. Historical climate forcing has increased the likelihood of extreme precipitation coinciding with warm temperatures suitable for transmission in March in northwest Peru. In turn, extreme precipitation during Cyclone Yaku caused a majority of dengue cases in the epidemic, especially across warmer districts. Extreme weather, made more likely by climate change, is already having an impact on human health. Climate change is increasing the likelihood of extreme weather that can drive outbreaks of climate-sensitive diseases. For example, dengue burden has recently increased rapidly with unusually warm and wet conditions. However, linkages between historical climate change, extreme weather, and mosquito-borne disease have not been traced quantitatively. Here, we analyze the contribution of extreme precipitation to dengue in northwestern Peru during Cyclone Yaku in March 2023. Using generalized synthetic control methods, we estimate 60% of cases were attributable to extreme precipitation and more cyclone-attributable dengue cases occurred in warmer, more flood susceptible, and more urban districts. Historical climate forcing has increased the likelihood of concurrent extreme precipitation and warm temperatures suitable for dengue transmission in northwestern Peru in March by 189%. This case study is one of the first to estimate cases of mosquito-borne illness caused by extreme weather conditions and shows those conditions were made more likely by climate change.

This study presents the discovery and characterization of novel microscopic structures found on the surface of a rock sample collected from a stream in Pereira, Risaralda, Colombia. The structures, termed microcondrulos, exhibit spherical morphologies atypical of known terrestrial entities. Qualitative and semi-quantitative chemical analyses were performed using scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM-EDS). Results revealed a complex elemental composition, with up to forty-eight different elements detected, including C, O, N, Si, Ti, V, Ni, La, and Ce, among others. The microcondrulos were differentiated from known terrestrial contaminants such as pollen, bacteria, and various protist groups. Comparative analysis with previously reported stratospheric samples and micrometeorites suggests a possible non-terrestrial origin. These findings contribute to the ongoing discussion on the diversity of life and the potential for alternative biogenesis. A taxonomic proposal for these new entities is presented for the first time.

Bat-borne viruses are a threat to global health and have in recent history had major impacts to human morbidity and mortality. Examples include diseases such as rabies, Ebola, SARS-Cov-1, and SARS-Cov-2 (COVID-19). Climate change could exacerbate the emergence of bat-borne pathogens by affecting the distribution and abundance of bats in tropical ecosystems. Here we report an assessment of historical climate and vampire bat occurrence data for the last century, which revealed a relationship between climatic variation and risk of disease spillover triggered by changes in bat distributions. This report represents one of the first examples of empirical evidence of global change effects on continental patterns of bat-borne pathogen transmission risk. We therefore recommend that more research is necessary on the impacts of climate change on bat-borne pathogen spillover risk, and that climate change impacts on bat-borne disease should be considered in global security initiatives. HighlightsO_LIBat-borne viruses are a threat to global health and include diseases such as rabies, Ebola, SARS-Cov-1, and SARS-Cov-2 (COVID-19). C_LIO_LIClimate change could exacerbate the emergence of bat-borne pathogens by affecting the distribution and abundance of bats. C_LIO_LIHere we report an assessment of historical climate and vampire-bat occurrence data for the last century, which reveals a relationship between climatic variation and risk of disease spillover triggered by changes in bat distributions. C_LI

As new cases of COVID-19 are being confirmed pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub-Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

Climate change detection and attribution (D&A) methods have become vital for quantifying the influence of anthropogenic forcing on the Earth's systems, including human health. Health impact attribution (HIA) studies seek to disentangle climate-driven health effects from natural variability yet are often constrained by the availability of accessible counterfactual climate scenarios. This tutorial paper presents a flexible, reproducible framework for developing counterfactual climates without reliance on computationally intensive global circulation models. We provide practical, R-based methodologies for constructing both trend-based (temperature and non-temperature) and event-based counterfactual, using a variety of techniques including model residual detrending, data-driven decomposition (e.g., Singular Spectrum Analysis and Empirical Mode Decomposition) and stochastic weather generators. The tutorial also explores the incorporation of greenhouse gas concentrations as forcing variables, rather than global mean temperature anomalies. By operationalising these methods through worked examples and an open code repository, this paper aims to build capacity within the HIA community, enhance methodological transparency, and foster interdisciplinary collaboration between climate and health researchers.

BackgroundLyme disease is the most common vector-borne illness in the United States. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and species diversity. ObjectiveTo determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of Lyme disease in the northeastern USA, 2000-2018. Materials and MethodsWe conducted an ecological analysis of incidence rates in counties of 15 "high" incidence states and the District of Columbia for 2000-2018. Annual counts of Lyme disease by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of Lyme disease NDVI, land cover variables, human population density, and calendar year. ResultsWe found that incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a u-shaped" association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed "n"-shaped relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category, increased standard deviation of NDVI showed the greatest increase in rates. DiscussionThese associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates. Public health interventions could make use of these results in informing people where risks may be high.

Hydroclimate whiplash, defined as the rapid transition between unusually wet and dry conditions, is expected to intensify under climate change, yet its population health impacts remain largely unknown. Here we quantified the association between hydroclimate whiplash and mortality across the contiguous United States from 2003 to 2023 using monthly county-level mortality records, standardized precipitation evapotranspiration index data, and two-stage time-series models. We identified overall and direction-specific dry-to-wet and wet-to-dry whiplash events at seasonal and sub-annual timescales and across 5-, 10-, and 20-year recurrence intervals. More severe whiplash events were associated with higher all-cause mortality risk; 5-, 10-, and 20-year sub-annual overall whiplash events increased mortality risk over five months by 3.4%, 4.5%, and 5.7%, respectively. Elevated risks were observed across cause-specific mortality outcomes, with the strongest association for infectious diseases. We estimated that 103,471 deaths were attributable to overall whiplash during the study period. These findings identify hydroclimate whiplash as an emerging climate-related public health threat and suggest that adaptation strategies focused on single hazards may underestimate the health burden of rapid, sequential hydroclimatic extremes.

West Nile virus (WNV; Flaviviridae) and Eastern Equine Encephalitis virus (EEEV; Togaviridae) represent the two most significant mosquito-borne zoonoses in the southeastern United States. While both viruses utilize avian amplifying hosts and mosquito vectors, it is unclear if they occur within distinct ecological niches. This study assessed the cumulative effects of both landscape composition and weather variables on the spatial-temporal distribution of WNV and EEEV outbreaks in Georgia over a twenty-four-year period (2001-2025). We used a modeling framework that directly accounts for both spatial and temporal effects but additionally integrates key EO variables (Earth observation). These environmental effects were investigated using Bernoulli generalized linear mixed-effects models (GLMMs) and executed with an integrated nested Laplace approximation (INLA). Our findings revealed disproportional distribution ranges of Culex quinquefasciatus, Aedes albopictus and Culiseta melanura in highly urbanized Georgian counties such as Chatham, Dekalb, and Fulton. Results showed that WNV transmission was heavily influenced by urban land cover (Posterior Mean: +0.78, 95% CrI: [0.61,0.95]) but negatively associated with lagged precipitation (Posterior Mean: -0.18, 95% CrI: [-0.29, -0.07]), confirming drought-driven amplification. Conversely, EEEV transmission was strongly influenced by wetland cover (Posterior Mean: +1.12, 95% CrI: [0.94,1.30]) and precipitation (Posterior Mean: +0.51, 95% CrI: [0.39,0.63]). These results underscore the need for improved mosquito surveillance across all Georgian counties in the face of growing vector-borne disease risks.

Escalating wildfire frequency and severity, exacerbated by shifting climate patterns, pose significant ecological and economic challenges. Prescribed burns, a common forest management tool, aim to mitigate wildfire risks and protect biodiversity. Nevertheless, understanding the impact of prescribed burns on soil and microbial communities in temperate mixed forests, considering temporal dynamics and slash fuel types, remains crucial. Our study, conducted at the University of Tennessee Forest Resources AgResearch and Education Center in Oak Ridge, TN, employed controlled burns across various treatments, and the findings indicate that low-intensity prescribed burns have none or minimal short-term effects on soil parameters but may alter soil nutrient concentrations, as evidenced by significant changes in porewater acetate, formate, and nitrate concentrations. These burns also induce shifts in microbial community structure and diversity, with Proteobacteria and Acidobacteria increasing significantly post-fire, possibly aiding soil recovery. In contrast, Verrucomicrobia showed a notable decrease over time, and other specific microbial taxa correlated with soil pH, porewater nitrate, ammonium, and phosphate concentrations. Our research contributes to understanding the intricate relationships between prescribed fire, soil dynamics, and microbial responses in temperate mixed forests in the Southern Appalachian Region, which is valuable for informed land management practices in the face of evolving environmental challenges.

BackgroundUnderstanding and projecting the spread of COVID-19 requires reliable estimates of the impact of weather on the transmission of the virus. Prior research on this topic has been inconclusive. We estimate the impact of weather on transmission by assembling one of the largest datasets integrating COVID-19 infections and weather, and use the results to project weather impact on transmission in the coming months. MethodsWe assemble a dataset that includes virus transmission and weather data across 3,739 locations from December 12, 2019 to April 22, 2020. Using simulation, we identify key challenges to reliable estimation of weather impacts on transmission, design a statistical method to overcome these challenges, and validate it in a blinded simulation study. Controlling for location-specific response trends we then estimate how different weather variables are associated with the reproduction number for COVID-19. We then use the estimates to project the relative weather-related risk of COVID-19 transmission across the world and in large cities. ResultsWe show that the delay between exposure and detection of infection complicates the estimation of weather impact on COVID-19 transmission, potentially explaining significant variability in results to date. Correcting for that distributed delay and offering conservative estimates, we find a negative relationship between temperatures above 25 degrees Celsius and estimated reproduction number ([Formula]), with each degree Celsius associated with a 3.1% (95% CI: 1.5-4.8%) reduction in [Formula]. Higher levels of relative humidity strengthen the negative effect of temperature above 25 degrees. Moreover, one millibar of additional pressure increases [Formula] by approximately 0.8 percent (0.6-1%) at the median pressure (1016 millibars) in our sample. We also find significant positive effects for wind speed, precipitation, and diurnal temperature on [Formula]. Sensitivity analysis and simulations show that results are robust to multiple assumptions. Despite conservative estimates, weather effects are associated with a 43% change in [Formula] between the 5th and 95th percentile of weather conditions in our sample. ConclusionsThe results provide evidence for the relationship between several weather variables and the spread of COVID-19, finding a negative association between temperature and humidity and transmission. However, the (conservatively) estimated effects of summer weather are not strong enough to seasonally control the epidemic in most locations.

BackgroundClimate change is an escalating health crisis, yet its influence on medical students career decisions remains underexplored. ObjectiveThis study uses the "Six Americas" framework to assess students climate views and the impact on residency and career decisions. MethodsA survey was distributed to students at four North American medical schools from October 2024 to January 2025. The instrument assessed demographic information, climate change views utilizing the Six Americas Super Short Survey, and the impact of climate change on residency and career decisions. Respondents were classified as Alarmed, Concerned, Cautious, Disengaged, Doubtful, or Dismissive using the Six Americas framework. Associations between climate attitudes, training year, and intended specialty were analyzed using Kruskal-Wallis tests. ResultsA total of 105 medical students completed the survey. 93% agreed climate change is a major health threat, 66% valued residency program sustainability efforts, and 41% indicated commitment to sustainability influences their rank list. Most students were Alarmed (62%), Concerned (19%), or Cautious (12%). Climate attitudes did not vary by specialty interest. Training year was associated with Six Americas classification (p < 0.05), MS2s had the most Alarmed respondents. ConclusionsMedical students overwhelmingly view climate change as a serious health threat and expect residency programs to demonstrate commitment to sustainability. Nearly half consider environmental values when ranking programs. These findings suggest climate-conscious training environments may gain a competitive recruitment advantage. Given the high proportion of "Alarmed" students, embedding climate-resilient education and sustainable practices into residency programs may align with future physician priorities.

Recently, a novel coronavirus (COVID-19) has caused viral pneumonia worldwide, spreading to more than 200 countries, posing a major threat to international health. To prevent the spread of COVID-19, in this study, we report that the city lockdown measure was an effective way to reduce the number of new cases, and the nitrogen dioxide (NO2) concentration can be adopted as an environmental lockdown indicator. In China, after strict city lockdown, the average NO2 concentration decreased 55.7% (95% confidence interval (CI): 51.5-59.6%) and the total number of newly confirmed cases decreased significantly. Our results also indicate that the global airborne NO2 concentration steeply decreased over the vast majority of COVID-19-hit areas based on satellite measurements. We found that the total number of newly confirmed cases reached an inflection point about two weeks after the lockdown. The total number of newly confirmed cases can be reduced by about 50% within 30 days of the lockdown. The stricter lockdown will help newly confirmed cases to decline earlier and more rapidly. Italy, Germany and France are good examples. Our results suggest that NO2 satellite measurement can help decision makers effectively monitor control regulations to reduce the spread of COVID-19.

In climate-health impact projection studies, projected impacts from multiple general circulation models (GCMs) are commonly aggregated by reporting the mean of GCM-specific impacts as the point estimate alongside a 95% empirical confidence interval (eCI) constructed from the 2.5th and 97.5th percentiles of the simulated pooled distribution of GCM-specific impacts. This study shows that the eCI generally does not yield the nominal coverage probability for the GCM-ensemble mean and constructs an interval aligned with the estimand. In a simulation study, the coverage of the eCI for the GCM-ensemble mean deviates from the nominal level in both directions, whereas the aligned interval yields coverage near 95% across all considered settings. The exact coverages derived analytically under a location-shift model agree with the simulation results. In a reanalysis of a heat-related mortality projection in London, the eCI is consistently wider. The eCI should be distinguished from confidence intervals for the GCM-ensemble mean; rather, the interval may be better described as a simulation-based approximate prediction interval for a GCM-specific impact under the uniformly randomly selected GCM from the considered GCM set.