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The increase in computational resources has enabled the emergence of multi-model ensembles of convection-permitting regional climate model (CPRCM) simulations at very high horizontal resolutions. An example is the CORDEX Flagship Pilot Study on “Convective phenomena at high resolution over Europe and the Mediterranean”, a set of kilometre-scale simulations over an extended Alpine domain. This first-of-its-kind multi-model ensemble, forced by the ERA-Interim reanalysis, can be considered a benchmark dataset. This study uses a recently proposed metric to determine the added value of all the available Flagship Pilot Study hindcast kilometre-scale simulations for maximum and minimum temperature. The analysis is performed using state-of-the-art gridded and station observations as ground truth. This approach directly assesses the added value between the high-resolution CPRCMs against their driving global simulations and coarser resolution RCM counterparts. Overall, models display some modest gains, but also considerable shortcomings are exhibited. In part, these deficiencies can be attributed to the assimilation of temperature observations into ERA-Interim. Although the gains for the use of kilometre-scale resolution for temperature are limited, the improvement of the spatial representation of local atmospheric circulations and land–atmosphere interactions can ultimately lead to gains, particularly in coastal areas.

A study was conducted with an objective to analyze how often high temperature events occur during the reproductive phases of wheat (January-March) in Punjab. Historical temperature data was analyzed to understand the probability of occurrence of temperature higher than the mean and its different combinations (mean+0.5SD, mean+1.0SD, mean+1.5SD and mean+2SD) during different standard meteorological weeks (SMW). It was found that at Ludhiana (central Punjab) the highest probability of maximum temperature (Tmax) and minimum temperature (Tmin) being higher than range was 16.9% and 18.8% during 9th and 4th SMW, respectively. At Ballowal Saunkhri (northeastern Punjab) the maximum probability of occurrence of Tmax higher than range was 16.4% during 6th and 12th SMW, respectively and that for Tmin higher than range was 20% during 9th SMW. At Bathinda (southwestern Punjab) the highest probability of occurrence of Tmax and Tmin above range was 19.7% and 19.4% during 13th and 11th SMW, respectively. In northeastern and southwestern regions of Punjab the probability of having Tmax and Tmin above range was maximum during 12-13th and 9th-11th SMW, respectively, while in central region it was maximum during 9th and 4th SMW, respectively. This implies that wheat crop should be managed adequately during these periods to avoid damage due to heat stress.

We investigate the time trends in the maximum and minimum temperatures in the United States from 1895 to 2017 using techniques that allow for fractional integration in the detrended series. In doing so we get more accurate estimates of the trends than those obtained using standard methods that impose either stationarity I(0) or nonstationarity I(1). Our results reveal evidence of significant positive trends in both maximum and minimum temperatures, while the difference between them show a significant negative trend as a consequence of the higher increase in the minimum temperatures. Evidence of stationary long memory behavior is also found in the three series examined. Time series plots and estimated time trends.

Recent studies have revealed a rise in extreme heat events worldwide, while extreme cold has reduced. It is highly likely that human-induced climate forcing will double the risk of exceptionally severe heat waves by the end of the century. Although extreme heat is expected to have more significant socioeconomic impacts than cold extremes, the latter contributes to a wide range of adverse effects on the environment, various economic sectors and human health. The present research aims to evaluate the contemporary spatio-temporal variations of extreme cold events in Southeastern Europe through the intensity–duration cold spell model developed for quantitative assessment of cold weather in Bulgaria. We defined and analyzed the suitability of three indicators, based on minimum temperature thresholds, for evaluating the severity of extreme cold in the period 1961–2020 across the Köppen–Geiger climate zones, using daily temperature data from 70 selected meteorological stations. All indicators show a statistically significant decreasing trend for the Cfb and Dfb climate zones. The proposed intensity–duration model demonstrated good spatio-temporal conformity with the Excess Cold Factor (ECF) severity index in classifying and estimating the severity of extreme cold events on a yearly basis.

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the “raw” GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the “raw” GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the “raw” GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.

Daily maximum, TX, and minimum, TN, temperatures recorded at Fabra Observatory, for the period 1914–2020 (from June to September), have permitted to identify the daytime and night time heatwaves, HWs, at the Barcelona Metropolitan Region, BMR, along 107 years. Four heatwaves indices have been computed both for maximum and minimum temperatures: the number of events per season, HWN; their maximum duration, HWD; their frequency of occurrence, HWF; and the amplitude of the hottest day event, HWA. Trends in these indices have been obtained and their statistical significance quantified by means of the Mann-Kendall test. For the whole period (1914–2020), positive significant trends have been detected for the four indices, with a marked positive trend since year 2000 for TX, and since 1985 for TN. Nevertheless, when 31-year moving windows have been analysed, both positive and negative significant trends are obtained, being remarkable the change in trend signs around 1960s. The convenience of using the four HW indices is evaluated applying a Principal Component Analysis, PCA, obtaining two principal components. Additionally, the spatial distributions of the MODIS land surface temperatures, LST, corresponding to some extreme heatwaves, permits the detection of two hotspots in the BMR, one for TX and the other for TN. It is also worth mentioning that correlations between the Western Mediterranean Oscillation index, WeMOi, and HW episodes are detected, being notable that, at the beginning of these episodes, WEMOi values are usually lower. These correlations could improve the HWs forecasting.

Abstract The overlap region between the eastern fringe of the Asian westerly region and the temperate continental-monsoon climate transition zone is sensitive to climate changes and is characterized by fragile ecosystems. Uncovering the long-term historical climate variability patterns in this region is necessary. A standardized tree-ring width chronology was constructed based on the tree-ring samples collected from four representative tree species in four typical areas in the overlap region, and the 203- to 343-year annual mean minimum temperature series in the overlap region were reconstructed. The reconstructed series overlapped well with extreme climate events and low-temperature periods recorded in historical data. Therefore, the reconstructed model is stable and reliable. As suggested by the reconstructed series, the annual average minimum temperature in the overlap region changes sharply from east to west, and the periodicity change in the overlap region shows a trend of gradually weakening from the east and west ends to the middle. In the nineteenth century, the high-latitude area was in the high-temperature period, and the entire overlap region experienced significant low-temperature periods lasting 20–45 years until the 1950s. The western part had an earlier low-temperature period start time, a longer cooling duration, and a slower cooling rate than the central part. The overlap region experienced a significant warming period in approximately the last half-century, with temperatures increasing faster in the western and eastern parts than in the central part. The temperature variability in the overlap region was more intense in the last two centuries, with shorter periodicities and a larger proportion of cold periods. The central and western parts of the Asian westerly region, the mid- to high-latitude regions of the transition zone, and the overlap region experienced significant low-temperature periods or drastic cooling trends (the Little Ice Age) in the first half of the nineteenth century and significant warming trends afterwards due to global warming. The influences of these changes may have been exacerbated by the westerly circulation. The results of this study provide new insights into the use of dendroclimatology to extract temperature series in the Asian westerly region and the transition zone and a reference for research on global climate change.

Rain‐fed crop yields are heavily influenced by seasonal rainfall patterns and temperature, particularly during vegetative and reproductive growth stages. This study was conducted to investigate the effects of rainfall distribution indices (monthly, seasonal, and annual) on rain‐fed wheat and barley yields using polynomial regression analysis across six different locations with varying elevations in Chaharmahal and Bakhtiari province, Iran. Additionally, the economic feasibility of rain‐fed wheat and barley in all locations was evaluated. Results showed that the monthly rainfall distribution index could not accurately predict wheat/barley yield, where elevation exceeds 2000 m and the average annual minimum temperature is below 4°C (such as in Koohrang, Borujen, Shahrekord, and Farsan). Conversely, the monthly rainfall distribution index was able to predict the wheat/barley yield with high accuracy (R2 > 0.75) in locations with lower elevation and higher average annual minimum temperature (such as Lordegan and Ardal). Compared to seasonal rainfall indices, annual rainfall indices showed weaker predictive accuracy in all locations. Furthermore, a significant relationship (p‐value < 0.0001) with a high coefficient of determination (R2 > 0.80) was found between spring rainfall index, spring minimum temperature, and wheat/barley yield in all locations. Therefore, incorporating minimum mean air temperature with the spring rainfall index is recommended for yield prediction for all locations. Economic analysis revealed that the internal return rates in Borujen, Farsan, Lordegan and Ardal exceeded the bank interest rate (14%), indicating that cultivating wheat and barley in these four locations was profitable and economic. Moreover, an exponential relationship between the average annual temperature and internal return rate was also established, offering a useful tool for farmers and planners to estimate the internal return rate based on only the average annual temperature. The relationships between measured and predicted barley yield in all locations. Dash line represents 1:1 line.

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

Although many studies have analyzed the effects of urbanization on temperature changes, the urbanization's effect on temperature change remains controversial. Northeast China is the largest old industrial base in China, which experienced a rapid urbanization in the past decades. Under the background of climate change, understanding the changes of surface air temperature and urbanization effects on temperature changes in the Northeast China is important to predict climate change in China. By analyzing the historical climate data, our results suggested that minimum temperature (Tmin) over the Northeast China increased significantly (0.40°C decade^(-1)) from 1960 to 1989, but showed no significant change (-0.02°C decade^(-1)) during 1990 - 2016. Due to slight change of maximum temperature (Tmax), the diurnal temperature range (DTR) showed a significant decreasing trend before 1989 (-0.34°C decade^(-1)), but reached a stable level after 1990 (-0.06°C decade^(-1)). In Northeast China, urbanization had a significant warming effect on Tmin during the night-time, but had different effects on Tmax during the daytime under the different changes of solar radiation before and after 1990. There were moderate warming effects of urbanization on Tmax during the solar dimming period of 1960 - 1989, but weak cooling effects on Tmax during the solar stable period after 1990. Due to obvious warming effect of urbanization on Tmin, urbanization in Northeast China tends to result in an increase of mean temperature but a decrease of DTR.