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Large temperature spatio-temporal gradients are a common feature of Mediterranean climates. The Portuguese complex topography and coastlines enhances such features, and in a small region large temperature gradients with high interannual variability is detected. In this study, the EURO-CORDEX high-resolution regional climate simulations (0.11° and 0.44° resolutions) are used to investigate the maximum and minimum temperature projections across the twenty-first century according to RCP4.5 and RCP8.5. An additional WRF simulation with even higher resolution (9 km) for RCP8.5 scenario is also examined. All simulations for the historical period (1971–2000) are evaluated against the available station observations and the EURO-CORDEX model results are ranked in order to build multi-model ensembles. In present climate models are able to reproduce the main topography/coast related temperature gradients. Although there are discernible differences between models, most present a cold bias. The multi-model ensembles improve the overall representation of the temperature. The ensembles project a significant increase of the maximum and minimum temperatures in all seasons and scenarios. Maximum increments of 8 °C in summer and autumn and between 2 and 4 °C in winter and spring are projected in RCP8.5. The temperature distributions for all models show a significant increase in the upper tails of the PDFs. In RCP8.5 more than half of the extended summer (MJJAS) has maximum temperatures exceeding the historical 90th percentile and, on average, 60 tropical nights are projected for the end of the century, whilst there are only 7 tropical nights in the historical period. Conversely, the number of cold days almost disappears. The yearly average number of heat waves increases by seven to ninefold by 2100 and the most frequent length rises from 5 to 22 days throughout the twenty-first century. 5% of the longest events will last for more than one month. The amplitude is overwhelming larger, reaching values which are not observed in the historical period. More than half of the heat waves will be stronger than the extreme heat wave of 2003 by the end of the century. The future heatwaves will also enclose larger areas, approximately 100 events in the 2071–2100 period (more than 3 per year) will cover the whole country. The RCP4.5 scenario has in general smaller magnitudes.

As climate change intensifies, ambient temperatures have become a global concern, leading to an increasing number of studies examining the impact of temperature on human health. Extreme weather events, including heatwaves and cold spells, are becoming more frequent and severe. Numerous studies have highlighted the positive correlation between non-optimal ambient temperatures and mortality. Understanding these impacts is crucial for developing targeted public health interventions and accurately predicting the future health burden associated with climate variability. This study aims to estimate the relative risks and mortality burden associated with temperature extremes over the past decade, focusing on the contributions of both heat and cold, as well as mild and extreme temperatures, and identifying vulnerable populations. By doing so, filling a regional research gap in Guiyang. We collected the daily weather and mortality data from 2013 to 2023. Descriptive analysis was conducted to characterize overall weather patterns and mortality trends during the study period. A quasi-Poisson regression with a distributed lag non-linear model (DLNM), incorporating a 21-day lag and controlling for trends, air pollutants, and the day of the week, was applied to estimate the cumulative relative risks of non-accidental mortality due to non-optimal and extreme temperatures. We calculated attributable fractions and attributable numbers for heat and cold (defined as temperatures above and below the daily mean temperature), mild temperatures (defined using cutoffs at the minimum mortality temperature, with mild heat ranging from the minimum mortality temperature to the 97.5th temperature percentile and mild cold ranging from the 2.5th temperature percentile to the minimum mortality temperature) and extreme temperatures (defined as temperatures below the 2.5th temperature percentile for extreme cold and above the 97.5th temperature percentile for extreme heat). A total of 140,099 non-accidental deaths were included in the study.Temperature and mortality showed U-shaped associations, except for 0-64 years age group. For extreme low temperatures, the effects appeared in lag 2 to 4 days and lasted for approximately 18 days, peaking on lag day 5, yielding a cumulative relative risks (RRs) of 1.24% (95% CI 1.14% to 1.36%) for non-accidental mortality. For extreme high temperatures, the strongest effect was observed on the same day, with an RR of 1.18%(95% CI 1.03% to 1.35%). The attributable fraction of non-accidental mortality associated with non-optimal temperatures was 9.21% (95% eCI: 5.32% to 12.15%). The mortality burden from heat and cold was 5.55% (95% eCI: 2.04% to 8.59%) and 3.67% (95% eCI: 1.45% to 5.80%), respectively. Mild heat was responsible for the majority of the mortality burden. Extreme low temperatures had higher cumulative relative risk and a prolonged effect compared to extreme high temperatures. The attributable fraction associated with non-optimal temperatures was highest for respiratory-related deaths. Mild heat was responsible for the majority of the mortality burden. Additionally, males and the individuals aged 65 years and above were particularly vulnerable populations.

We use the 100-member Max Planck Institute Grand Ensemble (MPI-GE) to disentangle the contributions from colocated dynamic atmospheric conditions and local thermodynamic effects of moisture limitation as drivers of variability in European summer heat extremes. Using a novel extreme event definition, we find that heat extremes with respect to the evolving mean climate increase by 70% under a moderate warming scenario during the twenty-first century. With a multiple regression approach, we find that the dynamical mechanisms representing blocking and anticyclonic conditions are the main driver of variability in extreme European summer temperatures, both in past and future climates. By contrast, local thermodynamic drivers play a secondary role in explaining the total variability in extreme temperatures. We also find that considering both dynamical and thermodynamical sources of variability simultaneously is crucial. Assessing only one type of drivers leads to an overestimation of their effect on extreme temperatures, particularly when considering only thermodynamical drivers. Lastly, we find that although most past and future heat extremes occur under favorable dynamical atmospheric conditions; this occurs 10–40% less frequently over Central Europe in the twenty-first century. By contrast, heat extremes over Central Europe occur 40% more frequently under concurrent extreme moisture limitation in the twenty-first Century. Our findings highlight a new type of neutral-atmosphere, moisture-driven heat extremes, and confirm that the increase in European heat extremes and associated variability increase are dominated by the local thermodynamic effect of moisture limitation.

The objective of this paper is to review statistical methods, dynamics, modeling efforts, and trends related to temperature extremes, with a focus upon extreme events of short duration that affect parts of North America. These events are associated with large scale meteorological patterns (LSMPs). The statistics, dynamics, and modeling sections of this paper are written to be autonomous and so can be read separately. Methods to define extreme events statistics and to identify and connect LSMPs to extreme temperature events are presented. Recent advances in statistical techniques connect LSMPs to extreme temperatures through appropriately defined covariates that supplement more straightforward analyses. Various LSMPs, ranging from synoptic to planetary scale structures, are associated with extreme temperature events. Current knowledge about the synoptics and the dynamical mechanisms leading to the associated LSMPs is incomplete. Systematic studies of: the physics of LSMP life cycles, comprehensive model assessment of LSMP-extreme temperature event linkages, and LSMP properties are needed. Generally, climate models capture observed properties of heat waves and cold air outbreaks with some fidelity. However they overestimate warm wave frequency and underestimate cold air outbreak frequency, and underestimate the collective influence of low-frequency modes on temperature extremes. Modeling studies have identified the impact of large-scale circulation anomalies and land–atmosphere interactions on changes in extreme temperatures. However, few studies have examined changes in LSMPs to more specifically understand the role of LSMPs on past and future extreme temperature changes. Even though LSMPs are resolvable by global and regional climate models, they are not necessarily well simulated. The paper concludes with unresolved issues and research questions.

Extreme heat poses significant threats to human life and ecosystems. Quantifying the effects of anthropogenic climate change on extreme heat has remained challenging, in part due to the short observational record. Here, we isolate the most slowly varying component of the frequency at which the historical 90th and 99th percentiles were exceeded in observational records from 1955 to 2021 by using a statistical method called low‐frequency component analysis. The emerging spatiotemporal signal in the changing frequency of temperature extremes can be attributed to a shift of the temperature distribution by local warming of the annual‐mean daily maximum temperature. The shift explains over 80% of the interannual variability in the frequency at which the historical 90th percentile is exceeded in the tropics and up to 50% in higher latitudes. This work connects variability in the frequency of extreme surface temperatures to variability in mean local warming. Plain Language Summary Over the past few decades, regions across the globe have experienced substantial increases in surface temperature extremes, posing significant threats to human life, as well as critical agriculture and energy sectors. Due to the relatively short observational record, it has been difficult to disentangle the relative roles of natural variability and anthropogenic forcing in driving changes to temperature extremes. Here, we introduce a simple framework for understanding the increasing frequency of surface temperature extremes by employing a statistical method to isolate the most slowly changing, and hence most likely anthropogenic, component of surface temperature extremes. We find that the emerging signal in the changing frequency of temperature extremes is largely driven by a shift in the temperature distribution by mean local warming. The shift explains over 80% of the observed variability in exceeding the 90th percentile in the tropics and up to 50% in higher latitudes. It also explains why changes in the frequency of extremes appear to be more rapid than changes closer to the center of the temperature distribution. This work offers guidance for climate risk assessment and adaptation strategies by connecting variability in the frequency of extreme temperatures to variability in mean warming at a given location. Key Points Shifting the surface temperature distribution by mean local warming explains much of the frequency increase in observed temperature extremes Mean local warming explains 80% of the observed variability in 90th percentile exceedance in the tropics and up to 50% in higher latitudes Narrower temperature distributions in the tropics are associated with a larger increase in extreme heat frequency compared to midlatitudes

The present study investigated future temperature and precipitation changes over Greece using the Weather Research and Forecasting (WRF) model. WRF was driven by EC-EARTH over Greece at very high resolution for the historical period (1980–2004), along with projected simulations, in the near future (2025–2049) and far future (2075–2099) under the Representative Concentration Pathways 4.5 (RCP4.5) and 8.5 (RCP8.5). Climatic variables were produced at 5-km grid spacing and 6-h interval. The historical simulation was evaluated against the available station observations. The analysis showed that the model underestimated the maximum temperatures and slightly overestimated the minimum temperatures. Also, the model simulated a small dry bias in precipitation with an excellent representation of the spatial patterns. The model projections for temperature under the two emission scenarios compared to the historical simulation revealed a robust magnitude of future warming with the most pronounced changes predominantly over the eastern areas of the country under the RCP8.5 in the far future. Projected precipitation changes were more evident in the far future with an overall decrease of the annual precipitation all over the eastern part of the country (with islands included) with the most dramatic reductions (above 40%) of seasonal precipitation observed under RCP8.5. Increases in the number of hot days were found everywhere with more pronounced changes over the plain areas under RCP8.5 in the far future. Significant increases of dry days were projected over the eastern part of the mainland and more intensely under RCP8.5 in the far future.

This study evaluated the effect of extreme temperatures on events requiring emergency room visits (ERVs) for hypertensive disease, ischemic heart disease (IHD), cerebrovascular disease, and chronic kidney disease (CKD) for population stratified by sex and age living in Taiwan’s metropolitan city from 2000 to 2014. The distributed lag non-linear model was adopted to examine the association between ambient temperature and area-age-sex-disease-specific ERVs for a population aged 40 years and above. The reference temperature was defined by a percentile value to describe the temperature in each city. Area-age-sex-disease-specific relative risk (RR) and 95% confidence intervals (CI) were estimated in association with extreme high (99th percentile) and low (5th percentile) temperatures. Temperature-related ERV risks varied by area, age, sex, and disease. Patients with CKD tend to have comorbidities with hypertensive disease. All study populations with hypertensive disease have significant risk associations with extreme low temperatures with the highest RR of 2.64 (95% CI: 2.08, 3.36) appearing in New Taipei City. The risk of IHD was significantly associated with extreme high temperature for male subpopulation aged 40–64 years. A less significant association was observed between the risks of cerebrovascular disease with extreme temperature. The risk of CKD was most significantly associated with extreme high temperature especially for a subpopulation aged 40–64 years. All study subpopulations with hypertensive disease have significant risk associations with extreme low temperature. Male subpopulations were more vulnerable to extreme temperatures, especially for those aged 40–64 years.

Vegetation growth is adversely impacted by multiple climate extremes related to the water and thermal stress over the Tibetan Plateau (TP). However, it remains unknown at which stress level these climate extremes can trigger the abrupt shifts of vegetation response to climate extremes and result in the maximum vegetation response across TP. To fill this knowledge gap, we combined the hydrometeorological data and the satellite-derived vegetation index to detect two critical thresholds that determine the response of vegetation productivity to droughts, high-temperature extremes, and low-temperature extremes, respectively, during 2001–2018. Our results show that the response of vegetation productivity to droughts rapidly increases once crossing −1.41±0.6 standard deviation (σ) below the normal conditions of soil moisture. When crossing −2.98σ±0.9σ, vegetation productivity is maximum damaged by droughts. High-temperature extremes, which have the two thresholds of 1.34σ ±0.4σ and 2.31σ±0.4σ over TP, are suggested to trigger the strong response of vegetation productivity at a milder stress level than low-temperature extremes (two thresholds: −1.44σ±0.5σ and −2.53σ±0.8σ). Moreover, we found the compounded effects of soil moisture deficit in reducing the threshold values of both high- and low-temperature extremes. Based on the derived thresholds of climate extremes that impact vegetation productivity, Earth System Models project that southwestern TP and part of the northeastern TP will become the hotspots with a high exposure risk to climate extremes by 2100. This study deciphers the high-impact extreme climates using two important thresholds across TP, which advances the understanding of the vegetation response to different climate extremes and provides a paradigm for assessing the impacts of climate extremes on regional ecosystems.

We present the first Antarctic-wide analysis of extreme near-surface air temperatures based on data collected up to the end of 2019 as part of the synoptic meteorological observing programs. We consider temperatures at 17 stations on the Antarctic continent and nearby sub-Antarctic islands. We examine the frequency distributions of temperatures and the highest and lowest individual temperatures observed. The variability and trends in the number of extreme temperatures were examined via the mean daily temperatures computed from the 0000, 0600, 1200, and 1800 UTC observations, with the thresholds for extreme warm and cold days taken as the 5th and 95th percentiles. The five stations examined from the Antarctic Peninsula region all experienced a statistically significant increase (p < 0.01) in the number of extreme high temperatures in the late-twentieth-century part of their records, although the number of extremes decreased in subsequent years. For the period after 1979 we investigate the synoptic background to the extreme events using ECMWF interim reanalysis (ERA-Interim) fields. The majority of record high temperatures were recorded after the passage of air masses over high orography, with the air being warmed by the foehn effect. At some stations in coastal East Antarctica the highest temperatures were recorded after air with a high potential temperature descended from the Antarctic plateau, resulting in an air mass 5°–7°C warmer than the maritime air. Record low temperatures at the Antarctic Peninsula stations were observed during winters with positive sea ice anomalies over the Bellingshausen and Weddell Seas.

Irrigation has distinct impacts on extreme temperatures. Due to the carryover effect of soil moisture into other seasons, temperature impacts of irrigation are not limited to irrigated seasons. Focusing on the North China Plain, where irrigation occurs in both spring (March‐April‐May) and summer (June‐July‐August), with a higher proportion of irrigation water applied during spring, we investigate the impact of spring irrigation on summer extreme heat events. Based on partial correlation analysis of data products, we find positive correlations between spring and summer soil moisture, suggesting that spring irrigation‐induced water surplus persists into the following summer and affects regional climate by impacting surface energy partitioning. Regional climate simulations confirm cross‐seasonal climatic effects and show that spring irrigation reduces the frequency and intensity of summer extreme heat events by approximately −2.5 days and −0.29°C, respectively. Our results highlight the importance of the cross‐seasonal climatic effect of irrigation in mitigating climate extremes. Plain Language Summary Irrigation exerts a stronger impact on extreme temperatures than on mean temperatures. The North China Plain (NCP) is a typical winter wheat‐summer maize rotation planting area, where irrigation is necessary in both spring and summer, but with a higher proportion of irrigation water applied during spring. The climatic effects of spring and summer irrigation in the NCP are intertwined due to the carryover effects of soil moisture. Recently, the climatic effect of irrigation in the NCP has been extensively explored, whereas the cross‐seasonal effects of irrigation on summer extreme heat events have never been quantified. In this study, we employ the Weather Research and Forecasting model coupled with a demand‐driven irrigation algorithm to discern the effects of spring and/or summer irrigation on summer extreme heat events by means of idealized climate simulations. The results show that spring and summer irrigation significantly reduces the frequency and intensity of summer extreme heat events by approximately −6.5 days and −1.0°C, of which spring irrigation contributes about 38% and 30%, respectively. Our findings underline the importance of irrigation‐induced climate impacts in mitigating extreme heat events and emphasize that climate change adaptation planning in terms of irrigation must account for cross‐seasonal climatic effects. Key Points Effect of multi‐seasonal irrigation on summer extreme heat events is investigated Spring irrigation is beneficial for reducing summer extreme heat events Irrigation modulates the relationship between spring and summer soil moisture