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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.

Observational analysis indicates significant changes in some temperature extremes over China across the mid-1990s. The decadal changes in hot extremes are characterized as a rise in annual hottest day and night temperature (TXx and TNx) and an increase in frequencies of summer days (SU) and tropical night (TR). The decadal changes in cold extremes are distinguished by a rise in annual coldest day and night temperature (TXn and TNn) and a decrease in frequencies of ice days (ID) and frost days (FD). These decadal changes manifest not only over China as a whole, but also over individual climate sub-regions. An atmosphere-ocean-mixed-layer coupled model forced by changes in greenhouse gases (GHG) concentrations and anthropogenic aerosol (AA) emissions realistically reproduces the general spatial patterns and magnitudes of observed changes in both hot and cold extremes across the mid-1990s, suggesting a pronounced role of anthropogenic changes in these observed decadal changes. Separately, changes in GHG forcing lead to rise in TXx, TNx, TXn and TNn, increase in frequencies of SU and TR and decrease in frequencies of ID and FD over China through increased Greenhouse Effect with positive clear sky longwave radiation and play a dominant role in simulated changes of both hot and cold extremes over China. The AA forcing changes tend to cool Southern China and warm Northern China during summer via aerosol-radiation interaction and AA-induced atmosphere-cloud feedback and therefore lead to some weak decrease in hot extremes over Southeastern China and increase over Northern China. Meanwhile, AA changes lead to warming over China during winter through cloud feedbacks related to aerosol induced cooling over tropical Indian Ocean and western tropical Pacific, and also induce changes in cold extremes the same sign as those induced by GHG, but with weak magnitude.

Extreme climate events can cause large risks to ecosystems and human society in a short period. Investigating the changing trends of such events is essential for regional climate risk management. However, there is limited information on the regional assessment of the history and future trends of extreme climate events in Xinjiang, China. This study investigated the historical changes and projected trends of extreme climate events in Xinjiang based on observational data and Coupled Model Intercomparison Project phase 6 (CMIP6) model simulations. The results showed that the bias correction effectively reduced the bias of the CMIP6 model to the extreme climate indices simulation. During the period 1961–2014, the extreme indices representing warmth showed robust growth, while the extreme indices representing cold showed a robust decline. The intensity and frequency indices of extreme precipitation continued to increase, while consecutive dry days (CDDs) shortened and consecutive wet days (CWDs) lengthened. The changing trend of the extreme temperature indices had strong spatial consistency, while the changing trend of the extreme precipitation indices had obvious spatial heterogeneity. Based on the CMIP6 model simulations, the extreme climate indices in the twenty-first century were projected to continue the changing trend of the historical period (1961–2014). Compared with north Xinjiang (NXJ) and south Xinjiang (SXJ), the cold spell duration index (CSDI), cold nights (TN10p), cold days (TX10p), and extreme precipitation events in the Tianshan Mountains (TSM) were projected to experience stronger changes in the twenty-first century. The response of extreme temperature and extreme precipitation indices to global warming was approximately linear. Compared with SSP585, most extreme climate indices under the SSP245 scenario changed slightly in response to global warming. The superposition of the increase (decrease) in extreme warm (cold) events and the increase in extreme precipitation events will exacerbate the threat of extreme climate events to the agricultural and ecological security of the Xinjiang oasis, especially in the TSM. Given the limited water vapor sources and precipitation and the high rate of evapotranspiration, it is projected that the current situation of water shortages in Xinjiang will not be fundamentally changed.

Observed distributions of atmospheric temperature are non‐Gaussian. Therefore, moments beyond variance are necessary in determining the frequency of extreme temperature events. Here we propose a simple kinematic model for atmospheric mid‐latitude temperature variability based on symmetric advection from a non‐symmetric background temperature profile. We then use this model to derive analytical expressions for the higher order moments of temperature distributions. Our results show that nonzero skewness and kurtosis arise due to the nonlinearity of the time‐mean meridional temperature profile. The analytical model matches an idealized Held‐Suarez atmospheric model, indicating nonlinearity of time‐mean temperature in latitude is the dominant contribution to nonzero skewness and kurtosis in synoptic temperature variations. Model analysis further shows decrease in higher order moments due to climate change come roughly equally from changes in mixing length and changes in the background temperature profiles. Plain Language Summary As climate change increases the average temperature of the Earth, the frequency and intensity of extreme temperature events changes as well. Understanding these changing extremes requires more than just understanding how the mean temperature increases; it requires an understanding of the entire distribution of daily temperatures and how this distribution changes. In this work, we build a theory for how the distribution of daily temperature is related to the time‐average temperature, and test this theory in a numerical climate model. We show that temperature changes in time at one fixed location are related to how the time‐mean temperature varies in space. Intuitively, this means we experience hot days when the air above us comes from a region where it is hotter on average, and we experience cold days when the air above us comes from a region where it is colder on average. Thus, we link a quantity that is important but difficult to measure—the frequency of temperature extremes—to a quantity that is well measured and more easily projectable into the future—the time‐average (or climatological) temperature profile. Key Points An analytical expression of the higher order moments of atmospheric temperature distributions is derived Latitudinal nonlinearity in temperature explains skew and kurtosis in mid‐latitudes for Held‐Suarez model with current temperature profile Changes in wind and temperature profiles equally contribute to decreases in moments due to climate change

Understanding changes in temperature extremes in a warmer climate is of great importance for society and for ecosystem functioning due to potentially severe impacts of such extreme events. In this study, temperature extremes defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) from CMIP5 models are evaluated by comparison with homogenized gridded observations at 0.5° resolution across the Tibetan Plateau (TP) for 1961–2005. Using statistical metrics, the models have been ranked in terms of their ability to reproduce similar patterns in extreme events to the observations. Four CMIP5 models have good performance (BNU-ESM, HadGEM2-ES, CCSM4, CanESM2) and are used to create an optimal model ensemble (OME). Most temperature extreme indices in the OME are closer to the observations than in an ensemble using all models. Best performance is given for threshold temperature indices and extreme/absolute value indices are slightly less well modelled. Thus the choice of model in the OME seems to have more influences on temperature extreme indices based on thresholds. There is no significant correlation between elevation and modelled bias of the extreme indices for both the optimal/all model ensembles. Furthermore, the minimum temperature (Tmin) is significanlty positive correlations with the longwave radiation and cloud variables, respectively, but the Tmax fails to find the correlation with the shortwave radiation and cloud variables. This suggests that the cloud–radiation differences influence the Tmin in each CMIP5 model to some extent, and result in the temperature extremes based on Tmin.

Guided by the Paris Agreement, the IPCC Special Report on Global Warming of 1.5 °C reported potential risks of climate change at different global warming levels (GWLs). To provide fundamental information on future temperature extremes over Southeast Asia (SEA), projected changes in temperature extreme indices are evaluated for different seasons at 1.5 °C and 2 °C GWLs against the historical reference period of 1976–2005 based on the ensemble of CORDEX simulations. Results show that the temperature indices increase significantly across the Indochina Peninsula and Maritime Continent at both GWLs except for decreasing daily temperature range (DTR) in the dry season, with more pronounced magnitudes at 2 °C GWL. Moreover, the regionally averaged ensemble medians of the indices show various changes over different subregions. At 1.5 °C and 2 °C GWLs, most pronounced increases of threshold indices. i.e. summer days (SU) and tropical nights (TR), are projected in Sumatra and Sulawesi for both wet and dry seasons. The warm spell duration (WSDI) increases generally, with strongest magnitudes for Sumatra and Sulawesi (Philippines and Sulawesi) in the wet (dry) season. On the other hand, significant increases of warm days and nights can also be observed at 2 °C GWL compared to 1.5 °C, particularly in the dry season, suggesting the high sensitivity of temperature extremes over the SEA. The projected potentially conspicuous temperature extremes under global warming of 1.5 °C and 2 °C primarily concentrate on the densely populated coastal regions of the main islands, showing the necessity of restricting global warming to 1.5 °C aiming at the eradication and reduction of regional climate stress for the human system in the developing countries over the SEA.

This study investigates the relationship between all-cause mortality and extreme temperature events (ETEs) from 1975 to 2004. For 50 U.S. locations, these heat and cold events were defined based on location-specific thresholds of daily mean apparent temperature. Heat days were defined by a 3-day mean apparent temperature greater than the 95th percentile while extreme heat days were greater than the 97.5th percentile. Similarly, calculations for cold and extreme cold days relied upon the 5th and 2.5th percentiles. A distributed lag non-linear model assessed the relationship between mortality and ETEs for a cumulative 14-day period following exposure. Subsets for season and duration effect denote the differences between early- and late-season as well as short and long ETEs. While longer-lasting heat days resulted in elevated mortality, early season events also impacted mortality outcomes. Over the course of the summer season, heat-related risk decreased, though prolonged heat days still had a greater influence on mortality. Unlike heat, cold-related risk was greatest in more southerly locations. Risk was highest for early season cold events and decreased over the course of the winter season. Statistically, short episodes of cold showed the highest relative risk, suggesting unsettled weather conditions may have some relationship to cold-related mortality. For both heat and cold, results indicate higher risk to the more extreme thresholds. Risk values provide further insight into the role of adaptation, geographical variability, and acclimatization with respect to ETEs.

Northeast China (NEC) is one of the major crop producing regions in China, which is severely affected by persistently extreme temperatures during summer. Previous works focused on understanding the intensity of extreme temperatures, yet neglecting its duration and timing. Considering the critical role of 25–60‐day intraseasonal variability (ISV) in inducing persistent temperature anomalies, our investigation found that 25–60‐day ISVs over both subtropical jet (SJ) and polar front jet (PJ) regions regulate the NEC temperature via the modulation of humidity and related longwave radiation (LR). During positive (negative) phase of PJ ISV, advection of climatological potential vorticity by strong meridional winds within the cyclonic‐anticyclonic (anticyclonic‐cyclonic) circulation favors the wave train corresponding to negative (positive) phase of SJ ISV. This interaction further amplifies and prolongs the impacts of SJ and PJ ISVs nonlinearly, with temperature decreases during the configuration of positive SJ and negative PJ ISV becoming 83% larger, and 6 days longer.

The global mean surface temperature is rising and under climate change it is expected to increase in the future. The changes are not uniform across the world and the impact assessment at a regional level is, thus, a necessity. Bangladesh, a tropical low-lying monsoon region, is greatly at risk under climate change. However, no attempt has been carried out to assess the changes in frequency of extreme temperatures (return period values) under the backdrop of climate change which is required in practical applications. The aim of this study is to investigate the changes in temperature extremes: maximum (Tx) and minimum (Tn) at the daily time scale under climate change in Bangladesh. The multi-model ensemble methodology comprised of five general circulation models and two emission scenarios from CMIP6 framework was used to evaluate the impact in two future time horizons: 2021–2060 and 2061–2100. The L-moment based frequency analysis with annual maxima (minima) data was used to quantify extreme temperatures in terms of return levels. With this approach, the identification of a probability distribution is one important aspect which is investigated in this study. We have found that the generalized normal (GNO) distribution is quite appropriate of describing the temperature extremes. There is a significant increase in location parameter which signifies a uniform shift for the extreme tail of the distribution. There are no appreciable overall changes in scale and shape parameter. The temperature extremes (both Tx and Tn) are expected to increase noticeably compared to the present observed condition. There is a tendency to have a greater estimate of extremes in the far future than the near future. The greater estimate is also perceived under the high emission scenario in comparison to the medium emission scenario. The country average change in 20-year return period value of Tx by the end of this century is about 3–4.7 °C compared to the corresponding changes of 2.4 to 3.7 °C for Tn. The findings are expected to assist in the evaluation of climate change impacts and adaptation strategies in Bangladesh.

Temperature-related health metrics are often determined not only by temperatures but also by multiple climate variables. Temperatures compounded by other climate variables are of significant concern in the assessment of climate change impacts on public health. Temperatures, wind speeds and their combined effects are investigated here for a comprehensive study of how measured temperatures, perceived temperature, and their related extremes will change in China under climate change conditions. Future projections of combined temperatures and wind speeds over China are generated through the PRECIS regional climate modeling system. Results indicate that temperatures can increase nearly 6 °C over China by the end of the twenty-first century from the baseline period (1976–2005) without considering the wind speed changes. However, by considering the combined effect of temperature and wind speed, the perceived temperatures over China are projected to decrease by 4.8 °C relative to the observed values in the baseline period. This unexpected drop in the future perceived temperatures suggests the projected warming is likely to be offset to a large extent by a potential increase in wind speed. This may be related to the RCM’s high-resolution making the thermal contrast distribute at finer scales. The mechanism behind this result needs to be further investigated to help understand the related physical processes and the associated uncertainties at regional scales. As for low-temperature extremes, China is projected to experience an apparent decrease in the frequency and duration of extreme cold events in the future compared to the baseline period without considering the combined wind chill effect. Considering the wind chill effect, an opposite trend for extreme cold events is detected, with an increase by 21% in the frequency of temperatures below − 20 °C.