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In this work, we compare the rate of warming of summertime extreme temperatures (summer maximum value of daily maximum temperature; TXx) relative to the local mean (summer mean daily maximum temperature; TXm) over the Northern Hemisphere in observations and one set of large ensemble (LE) simulations. During the 1979–2021 historical period, observations and simulations show robust warming trends in both TXm and TXx almost everywhere in the Northern Hemisphere, except over the eastern U.S. where observations show a slight cooling trend in TXx, which may be a manifestation of internal variability. We find that the observed warming rate in TXx is significantly smaller than in TXm in North Africa, western North America, Siberia, and Eastern Asia, whereas the warming rate in TXx is significantly larger over the Eastern U.S., the U.K., and Northwestern Europe. This observed geographical pattern is successfully reproduced by the vast majority of the LE members over the historical period, and is persistent (although less intense) in future climate projections over the 2051–2100 period. We also find that these relative warming patterns are mostly driven by the local hydroclimate conditions. TXx warms slower than TXm in the hyper-arid, arid, semi-arid and moist regions, where trends in the partitioning of the turbulent surface fluxes between the latent and sensible heat flux are similar during regular and extreme hot days. In contrast, TXx warms faster than TXm in dry-subhumid regions where trends in the partitioning of the surface fluxes are significantly different between regular and extreme hot days, with a larger role of sensible heat flux during the extreme hot days.

Extreme climate events can pose huge threats to human safety and industrial and agricultural production. Therefore, investigating the time of emergence (TOE) of climate extremes is essential to policymakers, especially residents suffering from related climate disasters. However, there is still a lack of work focused on the TOE of climate extremes over the whole global land and aggregated climate subtypes. Here, we employed phase 5 of the Coupled Model Intercomparison Project (CMIP5) multi-models to calculate the TOE of 27 widely used Expert Team for Climate Change Detection and Indices (ETCCDI) indices for the sake of analyzing the intensity, frequency, and duration of extreme temperature and extreme precipitation characteristics comprehensively based on Köppen-Geiger climate classification. We found the temperature indices change prior to the precipitation indices in general, and are more uniformly in spatial distributions of TOE. Regionally, the warming and wetting trends are obvious in Tropical areas. Arid areas are likely to witness tiny changes for most precipitation indices. Temperate areas are the most similar climate subtype to the mean state of the global land. The substantially earlier TOE of precipitation indices in Continental and Polar areas highlights the need for an early warning system. The findings of this study will help policymakers take targeted preventive measures to deal with specific extreme events in an adaptation to local conditions.

Global warming has clearly affected the occurrence of extreme events in recent years. Here, we assess changes in the frequency of temperature extremes and their causes, using percentile-based indices. Cold extremes are defined as temperatures below the 10th percentile of daily minimum (TN10) and maximum (TX10) temperatures while hot extremes exceed the 90th percentile of daily minimum (TN90) and maximum (TX90) temperatures. We analyze Berkeley Earth Surface Temperature (BEST) for observed changes in the last four decades 1981-2020, for two extended seasons, boreal summer April–September (AMJJAS) and boreal winter October–March (ONDJFM), and evaluate results using several reanalysis data sets. For the attribution of causes we use CMIP6 climate model simulations, analyzing natural-only and anthropogenic-only forcings. We use an attribution method that accounts for climate modeling uncertainty in both amplitude and pattern of responses.The observations show detectable changes in both cold and hot extreme temperatures. Hot extremes have increased in all regions and in both seasons while cold extremes have decreased over the past decades. Our attribution analysis revealed anthropogenic forcings are robustly detectable and the main drivers of observed changes in all indices for all regions, consistently in all data sets. Contributions from natural forcings are found small and detectable only in a few regions mainly for daytime cold extremes in ONDJFM. Anthropogenic forcing contributed to an increase of 3.4 days per decade in TN90 and of 2.7 days per decade in TX90, on average, at the global scale. Regionally, the anthropogenic contribution caused a range of decrease of 2–4.7 days per decade in TN10, 1.5–3.6 days per decade in TX10 while it caused an increase of 2.2–4.8 days per decade for TN90 and 2–3.3 days per decade in TX90. Anthropogenic-only warming in ONDJFM is slightly less than in AMJJAS.

Nocturnal temperature is observed increasing with global warming. However, evidence on night-time non-optimal temperature on the risk of preterm birth (PTB) is limited, and the potential interactions with air pollution on PTB has not been well clarified. We therefore conducted a population-based retrospective cohort study to evaluate the effect of night-time temperature extremes on the risk of PTB and its interaction with air pollution. Records of 196,780 singleton births from 4 counties in Huai River Basin (2013–2018) were obtained. Gridded data on night-time temperature were collected from a high-quality Chinese Air Quality Reanalysis dataset. We used a multivariate logistic regression to evaluate the effects of night-time heat and cold exposure on the risk of PTB as well as its subtypes. Potential interactions between night-time temperature extremes and fine particulate matter

Background: Multiple characteristics of the urban environment have been shown to influence population health and health-related behaviours, though the distribution and combined effects of these characteristics on health is less understood. A composite measure of multiple environmental conditions would allow for comparisons among different urban areas; however, this measure is not available in Canada. Objectives: To develop an index of environmental quality for Canada’s largest urban areas and to assess the influence of population size on index values. Methods: We conducted a systematic search of potential datasets and consulted with experts to refine and select datasets for inclusion. We identified and selected nine datasets across five domains (outdoor air pollution, natural environments, built environments, radiation, and climate/weather). Datasets were chosen based on known impacts on human health across the life course, complete geographic coverage of the cities of interest, and temporal alignment with the 2016 Canadian census. Each dataset was then summarized into dissemination areas (DAs). The Canadian Environmental Quality Index (Can-EQI) was created by summing decile ranks of each variable based on hypothesized relationships to health outcomes. Results: We selected 30 cities with a population of more than 100,000 people which included 28,026 DAs and captured approximately 55% of the total Canadian population. Can-EQI scores ranged from 21.1 to 88.9 out of 100, and in Canada’s largest cities were 10.2 (95% CI: −10.7, −9.7) points lower than the smallest cities. Mapping the Can-EQI revealed high geographic variability within and between cities. Discussion: Our work demonstrates a valuable methodology for exploring variations in environmental conditions in Canada’s largest urban areas and provides a means for exploring the role of environmental factors in explaining urban health inequalities and disparities. Additionally, the Can-EQI may be of value to municipal planners and decision makers considering the allocation of investments to improve urban conditions.

This study investigates mesoscale mechanisms associated with two extreme heatwaves affecting Santa Barbara County (SBC), southern California, and their implication for severe fire weather. We examine these issues using a database consisting of surface stations, radiosoundings, and 1 km grid spacing simulations with the Weather Research & Forecasting (WRF), including a climatology spanning 32 years. During the first heatwave event, synoptic conditions induced downslope winds on the southern-facing slopes of the Santa Ynez Mountains (SYM) on July 6, 2018. One surface station hit an all-time record, and nine surface stations exceeded 99.9th percentiles of surface temperatures. A wildfire (the Holiday Fire) erupted on the slopes of the SYM driven by high temperatures, low relative humidity, and strong winds. The nearby radiosonde registered temperatures at 850 hPa that exceeded the 95th percentile historical records (62 yrs). WRF simulations indicated that mountain wave activity contributed to the excessive surface temperatures on the south-facing slopes of the SYM, and explained the late evening timing for the maximum daily temperatures. The second heatwave broke all-time temperature records at 10 surface stations across SBC on September 6, 2020. Maximum temperatures for most of the SBC occurred during mid-afternoon, the highest observed temperature at the surface was 48.3 °C (118.9∘F), and the 850 hPa temperatures exceeded the 99th percentile. The September 2020 event occurred under weaker synoptic forcing (pressure gradients) than the July 2018 event, resulting in weaker winds in coastal Santa Barbara (including the slopes of the SYM) and Santa Ynez Valley. Nonetheless, the extreme heat and low relative humidity increased the Fosberg Fire Weather Index (FFWI) at critical values for a few hours in some sites when winds were moderate. To evaluate the relative importance of these extreme events in the historical context and to assess the region’s wildfire risk we propose a novel diagram based on the joint behavior of winds, temperature, humidity and FFWI. While no wildfires have broken out during the September 2020 heatwave, our analysis suggests that a combination of extreme heat with stronger winds would lead to unprecedented fire danger. These extreme conditions may become more common in a warming planet.

In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails. To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

Extreme weather is becoming more frequent due to drastic changes in the climate. Despite this, the body of research focused on the association between temperature extreme events and sperm quality remains sparse. In this study, we elucidate the impact of exposure to environmental temperature extremes on sperm quality. Data for this investigation were derived from the Anhui Prospective Assisted Reproduction Cohort, encompassing the period from 2015 to 2020. Parameters such as sperm concentration, total sperm count, total motility, progressive motility, total motile sperm count, and progressive motile sperm count were quantified from semen samples. We assessed the exposure of participants to temperature extremes during the 0-90 days prior to sampling. This investigation encompassed 15,112 participants, yielding 28,267 semen samples. Our research findings indicate that exposure to low temperature extreme for three consecutive days (at the first percentile threshold) has a detrimental correlation with sperm count parameters and concentration. Similar trends were observed with the second percentile threshold, where significant adverse effects typically manifested after a four-day exposure sequence. Analysis of high temperature extreme showed that exposure at the 98th percentile had adverse effects on all six sperm quality parameters, and the sperm count parameter was particularly sensitive to high temperature, showing significant results immediately after three days of exposure. When considering even more temperature extreme (99th percentile), the negative consequences were more pronounced on the sperm count parameter. Additionally, progressive motility showed a stronger negative response. In summary, parameters associated with sperm count are particularly vulnerable to temperature extremes exposure. Exposure to high temperature extremes environments may also be associated with a decrease in sperm concentration and vitality. The findings of this study suggest that male population should pay attention to avoid exposure to temperature extreme environment, which has important significance for improving the quality of human fertility., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The increasing trend of extreme heat events poses serious implications for rice production in China, especially when it coincides with the heat-sensitive reproductive growth period (RGP) leading to high temperature-induced spikelet sterility. (2) Methods: This study analyzed the long-term meteorological data of Jiangsu province, located at lower reaches of Yangtze River Valley, to ascertain changing trends in temperature extremes. Moreover, stress days and nights were derived from temperature extremes, during RGP as a proxy of rice sterility in the province. The stress days were defined in three ways, with higher daily maximum (≥35 °C, hot day, HD), and minimum temperature (≥28 °C, hot night, HN) and days with both critical limits (severe hot day, SHD). (3) Results: The analysis of data revealed that a definite warming hiatus period in the 1970s and a transitional phase in 1980s were evident for daily maximum temperatures (TMX) in the province. Warm phases since 1990s were evident for both day and night temperature extremes. Asymmetric warming of TMX and daily minimum (TMN) temperatures reduced diurnal temperature range (DTR) over time. The slight negative trend for TMX and HD were observed, while trends for TMN, HN and SHD were strongly positive in the province with regional variations. (4) Conclusion: Observed increasing trends of TMN, HNs, and SHDs, as well as the decreasing DTR during rice RGP can be a threat to rice production in the province. Rice crop in the Southern Jiangsu is more vulnerable to higher temperature extremes while in the northern Jiangsu, decreasing DTR can be crucial. These findings could have useful implications for improving rice yield under changing climate.

Previous observational studies have shown that exposure to ambient temperature and air pollution were associated with the incidence of gestational diabetes mellitus (GDM). However, the susceptible time window of non-optimal temperature on GDM is still unknown, and the interaction with air pollution has not been examined. We conducted a prospective cohort study in Guangzhou, China to investigate the windows of susceptibility of temperature extremes and variability on the risk of GDM and to explore any interaction effect with air pollution. Daily maximum (Tmax), minimum temperature (Tmin) and diurnal temperature range (DTR) were obtained from Guangdong Meteorological Service. Distributed lag non-linear models with a logistic regression were applied to assess the effect of temperature extremes and DTR in different weeks of gestation on GDM. To examine the interaction effect, relative excess risk due to interaction index, attributable proportion and synergy index were calculated. There were 5,165 pregnant women enrolled, of which 604 were diagnosed with GDM (11.7%). Compared with a reference temperature (50th percentile of Tmax), we found that extreme high temperature (99th percentile of Tmax) exposure during 21st and 22nd gestational weeks was associated with an increased risk of GDM. Extreme low temperature (1st percentile of Tmax) exposure during 14th to 17th weeks increased the risk of GDM. We observed that per 1 °C increment of DTR during 21st to 24th weeks was associated with an elevated GDM risk. No interaction effect of temperature extremes or variability with air pollution on GDM were observed. Our results suggested that non-optimal temperature is an independent risk factor of GDM. The time window of susceptibility for extreme temperatures and DTR exposure on the risk of GDM generally occurred in second trimester of pregnancy. In the context of climate change, our study has important implications for reproductive health and justifies more research in different climate zones.

The Greater Accra region is the most urbanized region in Ghana. Over the last decade, the region has undergone rapid population shift and urban sprawl. This has modified the natural environment of the area and led to the formation and warming of Urban Heat Island (UHI). Previous studies have not established enough evidence on the existence of UHI phenomenon in the region. For this reason, this study examined the existence, magnitude, and spatial extent of UHI warming as well as its effect on temperature extremes in the region. The study used Landsat satellite imagery captured in 1991, 2002 and 2017, and in-situ daily minimum and maximum temperature data spanning the period 1980 to 2017. The satellite images were processed and analyzed using an integrated Geographic Information System (GIS) and remote sensing technique while extreme temperature indices were assessed with the ClimPACT2 software. The study found that UHI existed in the region with an increasing spatial coverage and a magnitude of about 4.07 ∘C, 5.79 ∘C, and 4.86 ∘C in 1991, 2002, and 2017 respectively. The built-up and bare land areas experienced the strongest effect of UHI warming. In addition, enhanced UHI warming effect caused an increase in the frequency and intensity of warm temperature extremes in the region. A faster increase in night time temperatures than day time resulted in a decrease in diurnal temperature range of the region. Considering the high rate of warming amidst rapid urban expansion, more warming is expected in the region. This is expected to exacerbate climate extreme events and weather-related health issues. It is also expected to increase energy consumption, air pollution and human discomfort.

Background: Children are more vulnerable than adults to climate-related health threats, but reviews examining how climate change affects human health have been mainly descriptive and lack an assessment of the magnitude of health effects children face. This is the first systematic review and meta-analysis that identifies which climate-health relationships pose the greatest threats to children., Objectives: We reviewed epidemiologic studies to analyse various child health outcomes due to climate change and identify the relationships with the largest effect size. We identify population-specific risks and provide recommendations for future research., Methods: We searched four large online databases for observational studies published up to 5 January 2023 following PRISMA (systematic review) guidelines. We evaluated each included study individually and aggregated relevant quantitative data. We used quantitative data in our meta-analysis, where we standardised effect sizes and compared them among different groupings of climate variables and health outcomes., Results: Of 1301 articles we identified, 163 studies were eligible for analysis. We identified many relationships between climate change and child health, the strongest of which was increasing risk (60 % on average) of preterm birth from exposure to temperature extremes. Respiratory disease, mortality, and morbidity, among others, were also influenced by climate changes. The effects of different air pollutants on health outcomes were considerably smaller compared to temperature effects, but with most (16/20 = 80 %) pollutant studies indicating at least a weak effect. Most studies occurred in high-income regions, but we found no geographical clustering according to health outcome, climate variable, or magnitude of risk. The following factors were protective of climate-related child-health threats: (i) economic stability and strength, (ii) access to quality healthcare, (iii) adequate infrastructure, and (iv) food security. Threats to these services vary by local geographical, climate, and socio-economic conditions. Children will have increased prevalence of disease due to anthropogenic climate change, and our quantification of the impact of various aspects of climate change on child health can contribute to the planning of mitigation that will improve the health of current and future generations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Detection and attribution studies have demonstrated that anthropogenic forcings have been driving significant changes in temperature extremes since the middle of the 20th century. Moreover, new methodologies have been developed for the attribution of extreme events that assess how human influence may have changed their characteristics. Here we combine formal statistical analyses based on optimal fingerprinting to attribute observed long term changes in temperature extremes with an ensemble-based approach for event attribution. Our analyses are applied to 16 indices constructed with daily temperature data that focus on different characteristics of extremes and together build up a more complete representation of historical changes in warm and cold extremes than previous studies. For each index we compute an annual value for all years of the post-1960 period using data from observations and experiments with a coupled Earth System model for the analysis of multi-decadal changes and a high-resolution atmospheric model for event attribution. The models indicate that anthropogenic forcings have influenced almost all indices in recent decades and led to more prominent changes in the frequency of extremes. The optimal fingerprinting analyses show that for most indices the anthropogenic signal is detectable in changes during 1961–2010 both in Europe and on a quasi-global scale. The weaker natural effect, resulting mainly from volcanic eruptions, is in most cases not detectable, with the exception of large scale changes in indices linked to the frequency of cold night-time extremes. Our event analyses estimate how anthropogenic forcings alter the chances of getting new record index values in Europe and find that such extremes would be markedly rare if human influence were not accounted for, whereas in the current climate their return times range from a few years to a few decades.

Short-term exposure to ambient high temperature (heat) could increase the risk of cardiovascular disease (CVD). However, available evidence on the burden of daytime and nighttime heat on CVD is limited and vulnerable populations remain unknown so far. We aimed to examine and differentiate the impact of daytime and nighttime heat on CVD in China. Data on daily outpatient visits for CVD were collected from 15 Chinese cities spanning multiple geographical regions, climates, and socio-economic conditions. The population-weighted temperature was used to calculate excess heat exposure in warm seasons (June-September) from 2011 to 2015. Hot day excess (HDE) and hot night excess (HNE), the sum of temperature above the heat threshold during daytime and nighttime respectively, were used to represent daytime and nighttime excess heat. A distributed lag non-linear model was employed to estimate the city-level association between HDE/HNE and daily CVD cases. The city-level association was then pooled by multivariate meta-analysis. We further estimated the disease burden of CVD attributable to HDE and HNE by geographical regions, gender, and age. A total of 729,409 cases of CVD were included in this study. Both HDE and HNE were associated with an increased risk of CVD, with greater effects from nighttime heat (relative risk (RR): 1.38; 95% confidence interval (CI): 1.18-1.61) than daytime heat (RR: 1.10; 95% CI: 1.05-1.15). The proportion of CVD cases attributable to HNE was 15.7%, which was almost three times as high as HDE (4.6%, p for difference <0.05). Males, people living in northern cities, and those aged less than 45 years were more vulnerable to HNE. Our findings for the first time revealed an intra-day difference in the heat effect on CVD, with a greater impact from nighttime heat exposure, which should be considered to protect vulnerable populations on hot days., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background/objective: Research suggests gestational exposure to particulate matter ≤2.5 μm (PM 2.5 ) and extreme heat may independently increase risk of birth defects. We investigated whether duration of gestational extreme heat exposure modifies associations between PM 2.5 exposure and specific congenital heart defects (CHDs). We also explored nonlinear exposure-outcome relationships., Methods: We identified CHD case children (n = 2824) and non-malformed live-birth control children (n = 4033) from pregnancies ending between 1999 and 2007 in the National Birth Defects Prevention Study, a U.S. population-based multicenter case-control study. We assigned mothers 6-week averages of PM 2.5 exposure during the cardiac critical period (postconceptional weeks 3-8) using the closest monitor within 50 km of maternal residence. We assigned a count of extreme heat days (EHDs, days above the 90th percentile of daily maximum temperature for year, season, and weather station) during this period using the closest weather station. Using generalized additive models, we explored logit-nonlinear exposure-outcome relationships, concluding logistic models were reasonable. We estimated joint effects of PM 2.5 and EHDs on six CHDs using logistic regression models adjusted for mean dewpoint and maternal age, education, and race/ethnicity. We assessed multiplicative and additive effect modification., Results: Conditional on the highest observed EHD count (15) and at least one critical period day during spring/summer, each 5 μg/m 3 increase in average PM 2.5 exposure was significantly associated with perimembranous ventricular septal defects (VSDpm; OR: 1.54 [95% CI: 1.01, 2.41]). High EHD counts (8+) in the same population were positively, but non-significantly, associated with both overall septal defects and VSDpm. Null or inverse associations were observed for lower EHD counts. Multiplicative and additive effect modification estimates were consistently positive in all septal models., Conclusions: Results provide limited evidence that duration of extreme heat exposure modifies the PM 2.5 -septal defects relationship. Future research with enhanced exposure assessment and modeling techniques could clarify these relationships., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Many studies have investigated the associations between heat waves, ambient temperature, cold spells, and mortality or morbidity. Some studies have utilized effect modification to reveal the factors that increase an individual's susceptibility to temperature extremes, which can then be used to reshape public policy. In this study, we used a time-stratified case-crossover technique to examine how individual- and regional-scale characteristics modified heat wave-related impacts on mortality rates in Seoul, South Korea, between 2009 and 2012. We defined a heat wave as having at least two consecutive days with a daily mean temperature greater than or equal to the 95th percentile recorded in each of Seoul's twenty-five districts. At the individual scale, citizens classified as belonging to a lower education group had a higher vulnerability to heat wave-related morbidity or mortality [odds ratio (OR) 1.261; 95% confidence interval (CI): 1.034-1.538]. At a regional scale, death during heat waves was more likely to occur in districts with a high deprivation index (OR=1.194; 95% CI: 1.028-1.388). And a low proportion of green space around buildings (OR=1.178; 95% CI: 1.016-1.366), a low proportion of rooftop green space (OR=1.207; 95% CI: 1.042-1.399), or those that had fewer hospitals (OR=1.186; 95% CI: 1.019-1.379). Our data show that mortality during heat waves is more likely where these individual and regional-scale vulnerabilities overlap. Our findings support evidence of mortality impacts from heat waves and provide a basis for selection to policy makers choose on the target groups to reduce the public health burden of heat waves., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Extreme heating (up to 43 °C measured from five-year temperature records) occurs in shallow coastal seagrass meadows of the Great Barrier Reef at low tide. We measured effective quantum yield (ϕPSII), growth, senescence and mortality in four tropical seagrasses to experimental short-duration (2.5h) spikes in water temperature to 35 °C, 40 °C and 43 °C, for 6 days followed by one day at ambient temperature. Increasing temperature to 35 °C had positive effects on ϕPSII (the magnitude varied between days and was highly correlated with PPFD), with no effects on growth or mortality. 40 °C represented a critical threshold as there were strong species differences and there was a large impact on growth and mortality. At 43 °C there was complete mortality after 2-3 days. These findings indicate that increasing duration (more days in a row) of thermal events above 40 °C is likely to affect the ecological function of tropical seagrass meadows., (Copyright © 2014 Elsevier Ltd. All rights reserved.)