Trends In Temperature Extremes Research Articles

Trends of Extreme Temperature and Rainfall Indices for Addis Ababa City, Ethiopia

The extreme temperature and precipitation index trend analysis for Addis Ababa city is presented in this study. The Bole and Tikur Anbesa observatories, two stations in the capital city of Addis Ababa, provide the temperature and rainfall variation used in this analysis. The RClim-Dex software has been used to find climate extreme indices for Addis Ababa city stations based on climate data for the period 1954 to 2019. The annual total precipitation trend (PRCPTOT) at the 5% significant level exhibits a positive slope at the Addis Ababa observatory and a negative slope at the Addis Ababa Bole station. SDII trends indicate an increase at the 5% significant threshold for both stations. At the 5% significance level, the trends for both stations’ maximum and lowest temperatures show a positive slope. Conversely, both stations exhibit a negative slope in the diurnal temperature ranges. The RClim-Dex results indicate that, while rainfall intensity is decreasing, there is a tendency for annual rainfall indices to increase at the Addis Ababa observatory station, but to decrease at the Bole station. Additionally, there is an upward slope for consecutive wet days for all stations. Nevertheless, steady warming patterns are evident for both the maximum and minimum temperature indices. In contrast, declining trends are shown in the diurnal temperature ranges.

Revealing trends in extreme heatwave intensity: applying the UNSEEN approach to Nordic countries

The increase in heatwave intensity, causing heat stress and crop failures in many regions is a concerning impact of global climate change. In northern Europe, significant interannual variability previously prevented robust assessments of trends in heat extremes. However, with a large-ensemble seasonal hindcasts and archived forecasts dataset covering 1981–2022 multiple realisations of weather patterns can be pooled and assessed. What are recent trends of extreme temperatures? Has the risk for a 100-year heatwave event increased in Northern Europe? We apply the UNSEEN (UNprecedented Simulated Extremes using ENsembles) approach to assess the credibility of the model ensemble and use non-stationary extreme value analysis to quantify recent trends in extreme 3-day heatwaves in late spring and early summer (May to July). We find significant non-stationarity and positive trends in annual maximum heatwave intensity. We also show that heatwave volatility, i.e. the risk of clearly outstanding heatwaves, is highest in central Scandinavia.

Spatiotemporal Trends of Extreme Temperature Events Along the Qinghai‐Tibet Plateau Transportation Corridor From 1981 to 2019 Based on Estimated Near‐Surface Air Temperature

AbstractThe intensification of global warming leads to the increased frequency of extreme temperature events. Many studies have reported on numerous regions facing extreme hot and cold temperatures to some degree. In this study, the daily maximum, minimum and mean near‐surface air temperatures were estimated to explore the detailed spatial trends of extreme temperature events along the Qinghai‐Tibet Plateau Transportation Corridor (QTPTC). Sixteen extreme temperature indices were used to represent extreme temperature events, and the Mann‐Kendall trend test and Sen's slope estimation method were employed to explore the spatiotemporal trends of extreme temperatures along the QTPTC during 1981–2019. The possible factors that affect the trends of extreme temperature events were also discussed, indicating that: (a) There was an exacerbation of extreme hot events, while the prevalence of extreme cold events weakened along the QTPTC. This was in accordance with the trend of global warming. (b) The trends of extreme temperature indices based on daily minimum temperature were stronger than those based on daily maximum temperature in most areas, except for the eastern region. Furthermore, as altitude increased, the amplitude of warming intensified. (c) Unlike the trend in most areas, the intensified warming trend occurred in the low‐altitude areas in the eastern region where there are more human activities and rapid urbanization, and the daily maximum temperature trend was more severe there. The results can provide references for policymakers to formulate corresponding adaptation strategies, addressing the impacts of extreme temperature events on transportation facilities with significant elevation differences.

Detecting Trends in Extreme Temperature and Precipitation events with Different Return Periods in

Detecting Trends in Extreme Temperature and Precipitation events with Different Return Periods in

Spatiotemporal Variation of Temperature Extremes over the Arctic Lands Based on In Situ and Reanalysis Data

Abstract Changes in extreme temperatures have more effects on ecosystems and human society than changes in climate averages. As a hotspot of global warming, the Arctic has experienced unprecedented heatwaves recently, which highlights the importance of identifying long-term variations of extreme temperatures. However, spatial unbalance of observations and artificially chosen investigation periods limit our knowledge of extreme temperatures over the Arctic lands. Here, we build a complete and quality-controlled observation network on surface temperature over the Arctic lands and combine in situ and reanalysis data to evaluate changes of extreme temperatures during the period 1979–2020. Our results indicate that 1) the increase in extreme temperatures has accelerated since the 2000s, especially on the coast of Eurasia; 2) the change magnitude for cold events is larger than for warm events, in terms of intensity, frequency, and duration; and 3) increases in warm events only occur locally, for example, Alaska and central Siberia, while decreases in cold events occur throughout the Arctic lands. The long-term trends of extreme temperatures are synchronous with sea ice loss, and patterns of interannual variations are mainly related to the North Atlantic Oscillation. We suggest further efforts toward improvement over North America, especially for Greenland, through sufficient observations and regional models.

Multifractal Features and Dynamical Thresholds of Temperature Extremes in Bangladesh

Multifractal detrended fluctuation analysis (DFA) can extract multi-scaling behavior and measure long-range correlations in climatic time series. In this study, with the help of multifractal DFA, we investigated the scaling behavior of daily minimum/maximum temperatures during the years 1989–2019 from 34 meteorological stations in Bangladesh. We revealed spatial patterns, topographic impacts and global warming impacts of long-range correlations embedded in small and large fluctuations in temperature time series. Meanwhile, we developed a multifractal DFA-based algorithm to dynamically determine thresholds to discriminate extreme and non-extreme events in climate systems and applied it to analyze the frequency and trends of temperature extremes in Bangladesh. Compared with widely-used percentile thresholds, the extreme climate events captured in our algorithm are more reliable since they are determined dynamically by the climate system itself.

Trends in climate change observed under tropical wet and tropical montane climates; A case study from Sri Lanka

Climate change-related changes in temperature and precipitation trends must be investigated at local, regional and global levels. Temperature and precipitation trends in two selected regions having tropical wet and tropical montane climates (i.e., Colombo and Nuwara Eliya respectively) in Sri Lanka were studied for a 30 year period from 1989 to 2019, to evaluate the temporal dynamics of climate change. Precipitation trends were analyzed on annual, monthly, and seasonal scales, while the trends in mean, minimum, and maximum temperatures were examined on annual and monthly scales. Decadal time series plots were used to study decadal variations in average temperature and precipitation. The trends in extreme temperature and precipitation events were also evaluated. In addition, the trends in diurnal temperature range (DTR), cool and warm nights, and heat index (HI) were studied. The significance of trends was evaluated using the Mann-Kendall test, while the magnitude of the slope was assessed by Sen’s slope estimator. Clear statistically significant increasing trends were observed for the mean annual temperatures under the tropical wet and tropical montane climates, and no clear trends were observed in annual precipitation in both districts. There were decreasing trends in south-west monsoon rainfall, with a significant decrease in Nuwara Eliya under the tropical montane climate. Increasing trends were observed for the average monthly precipitation in November (i.e., during the inter-monsoonal rains) and average monthly temperature in April (i.e., the hottest month) over the last decade (i.e., 2010-2019) in Colombo. The DTR has significantly decreased over the last three decades in Colombo. A significant upward trend was observed for HI values during the last decade in Colombo. Colombo also showed a statistically significant decreasing trend in the number of cool nights and a statistically significant decreasing trend in the number of warm nights over the last decade.

Observed Variations and Trends in Temperature Extremes of Longest Meteorological/Climatological Observation Station (Kandilli Observatory) in Turkey (1912-2021)

Bu çalışmada; Türkiye’nin en uzun meteorolojik/klimatolojik gözlemlerine sahip olan Kandilli (İstanbul) Rasathanesi’nde ekstrem sıcaklıkların 1912-2021 dönemindeki değişimleri ve eğilimleri analiz edilmiştir. Çalışmada İklim Değişikliği Tespiti, İzleme ve İndisler Konusunda Klimatoloji Uzman Ekibi (ETCCDMI) tarafından önerilen 14 ekstrem sıcaklık indisi kullanılmıştır. Ekstrem sıcaklık indislerinde olası eğilimleri belirlemede ve eğilimlerin istatistiksel olarak anlamlılığını denetlemede klimatoloji çalışmalarında yaygın olarak kullanılan parametrik olmayan Mann-Kendall (M-K) sıra ilişki katsayısı testinden yararlanılmıştır. Eğilimlerin büyüklüğünü belirlemek için de parametrik olmayan Sen’in Eğim Tahmin (Sen’s Slope Estimation) yöntemi kullanılmıştır. Bunun yanında rasat süresi içinde indislerdeki değişimi belirlemek amacıyla da ekstrem sıcaklık indisleri 1912-1950, 1951-1985 ve 1986-2021 olmak üzere üç alt dönemde incelenmiştir. Analizler sonucunda; soğuk günler ve soğuk geceler indislerinde her on yıl için sırasıyla -0.96 ve -1.04 gün olmak üzere anlamlı azalma eğilimi, sıcak günler ve sıcak geceler indislerinde ise sırasıyla +0.44 gün/on yıl, +0.58 gün/on yıl olmak üzere anlamlı artma eğilimleri saptanmıştır. En düşük gündüz hava sıcaklıkları, en düşük gece hava sıcaklıkları, en yüksek gündüz hava sıcaklıkları, en yüksek gece hava sıcaklıkları indislerinde her on yıl için sırasıyla +0.10, +0.18, +0.5, +0.14 °C olmak üzere artma eğilimleri belirlenmiştir. Minimum rekorlardaki artma eğilimleri istatistiksel olarak anlamlı olmuştur. Don olaylı günler indisinde -0.93 gün/on yıl olmak üzere anlamlı azalma eğilimi, yaz günü (+3.1 gün/on yıl) ve tropikal geceler (+3.4 gün/on yıl) indislerinde ise anlamlı artma eğilimleri tespit edilmiştir. Sıcak dönem süresi indisinde artma eğilimi, soğuk dönem süresi indisinde (-1.37 gün/on yıl) ise anlamlı azalma eğilimleri saptanmıştır. Bu sonuçlar, istasyonda maksimum sıcaklıkların arttığını, minimumların ise azaldığını ortaya koymakta ve bunlara bağlı olarak da sıcak ekstremlerin etkilerinin artığını soğuk ekstremlerinse azaldığını göstermektedir. Diğer taraftan bu sonuçlar, 20. yüzyılın ilk yarısının istasyonun bulunduğu bölge özelinde 1981-2010 normaline göre soğuk olduğunu ancak sıcaklıkların günümüze doğru antropojenik etkilere bağlı olarak giderek arttığını/değiştiğini göstermektedir.

Analysis of trends in extreme temperature indices over selected areas in Peninsular Malaysia from 1991-2020

This study investigates the temporal variations of extreme temperature trends across three stations in Peninsular Malaysia with different settings, Petaling Jaya represents urban station, while Kluang and Batu Embun represent rural areas from 1991-2020. Eleven extreme warm and cold indices trends were analysed using the Mann-Kendall test, with Sen’s slope as a magnitude estimator. The findings revealed that the annual daily mean temperature, daily mean maximum temperature, and daily mean minimum temperature increased significantly at all stations by 0.04°C yr–1, 0.02°C yr–1, and 0.05°C yr–1, respectively. The station in the rural area shows a significant increment of an average of 0.03°C yr–1 for mean and warm extreme indices. Despite that, there were no changes in the total number of warm days and nights and cold days and nights for all settings. For the cold temperature indices, there were significant decreasing trends for the annual minimum of maximum temperature in rural areas, while urban station did not exhibit changes. All stations also exhibit similar annual patterns when compared although significantly higher readings were observed during El Niño events.

On the Seasonal and Spatial Dependence of Extreme Warm Days in Antarctica

AbstractThe spatial distribution of trends in temperature extremes over Antarctica remains largely unknown. Here we investigate the seasonal and spatial characteristics of extreme warm occurrence across Antarctica. The Antarctic inland areas show significant positive trends in the number of extreme warm days in austral spring and summer. The trends in the seasonal mean of daily maximum temperature show strong coherence with the trends in extreme warm occurrences. In addition, the long‐term longwave radiation, water vapor flux and regional atmospheric circulation changes are closely connected to the trends of extreme warm days in all seasons outside of summer; the summer longwave radiation and interior wind trends show little coherence with warm extreme trends, indicating other processes at play driving extreme warm days in summer.

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

Early 21st Century Trends of Temperature Extremes over the Northwest Himalayas

The rising intensity and frequency of extreme temperature events are caused due to climate change and are likely to affect the entire world. In this context, the Himalayas are reported to be very sensitive to changes in temperature extremes. In this study, we investigate the variability of temperature extremes over the Northwest Himalayas in the early 21st century (2000–2018). Here, we used 14 temperature indices recommended by ETCCDI (Expert Team on Climate Change Detection and Indices). The present study reveals the trends of extreme temperature indices on the spatial scale for the western part of the Northwest Himalayas. The 14 temperature indices were used to assess the behavior of extreme temperature trends with their significance. This study reports that the northwestern region of the study area has a cooling effect due to an increase in the trends of cold spells, cold days/nights, and frost days, while the southwestern region significantly shows the warming effects due to the increasing trends in warm spells, warm days/nights, and summer days. On the other hand, the eastern region of the study area shows mixed behavior, i.e., some places show warm effects while some reveal cold effects in the early 21st century. Overall, this study implies the northwestern parts have cooling trends while the southwestern and southeastern parts have warming trends during the early 21st century.

Spatiotemporal analysis of temperature and precipitation extremes over Bangladesh using a novel gridded observational dataset

This study explores spatial changes in trends of 29 climate extremes indices between 1981 and 2020 in Bangladesh, a country that frequently experiences the impacts of extreme events. This is the first study to examine trends in extreme climate over Bangladesh using a high resolution (0.05° × 0.05°) gridded daily temperature and precipitation data rather than individual stations. Trends in mean daily maximum and minimum temperatures were +0.38 °C and +0.25 °C per decade, respectively, for the last 40 years. The maximum increases in both warm days and nights were 12.75 days per decade with the fastest rate of warming in the eastern part of the country, whereas the maximum decreases in both cold days and nights were 7 days per decade with the western part experiencing the greatest increase in extreme cold. Significant reductions in total precipitation (225 mm per decade) were observed in the major parts of the country. However, the number of very heavy precipitation days (R20mm) showed a statistically significant increase (2.25 days per decade) in the southern coastal areas, with the tendency of more extreme wet days (R30 mm). The maximum decrease in annual total precipitation was 225 mm per decade while the consecutive dry days increased by 12.5 days per decade. While previous studies reported a faster rise in minimum temperature than maximum temperature, this study shows a faster increase in maximum temperature instead of minimum temperature in the study area. Extreme temperature changes were statistically more significant than changes in extreme precipitation. This study also shows statistically significant and consistent spatial trends of extreme temperature and precipitation when using high-resolution gridded observational datasets instead of station data.

Mid-century change analysis of temperature and precipitation maxima in the Swat River Basin, Pakistan

This study analyzes trends in historical (1989–2018) and projected (2041–2060) temperature and precipitation maxima in the Swat River Basin, Pakistan. This basin has a history of climate-related disasters that directly affected livelihood and personal safety in local communities and are becoming more intense and more frequent due to changing climate. Major economic sources of this basin are agriculture and tourism, both highly sensitive to extreme climate events. Therefore, it is very important to assess future trends in extremes of temperature and precipitation. Non-parametric tests were employed for currently acquired data, while future projections were assessed using the statistical downscaling model (SDSM) with CanESM2 GCM under three scenarios: representative concentration pathways (RCPs) of 2.6, 4.5, and 8.5. The R2 value between monthly observed and simulated temperatures varied from 0.82 to 0.91 and 0.92 to 0.96 for training and confirmation periods, respectively. For areal precipitation, an R2 value of 0.49 was noted for calibration and 0.35 for validation. Observed temperatures showed a decreasing trend at all stations except Saidu Sharif, but the differences were not significant. Precipitation showed an increasing trend at two stations, Kalam and Malam Jabba, and a decreasing trend at two other stations, Dir and Saidu Sharif. A >2°C rise was noted for the annual projected maximum temperature (2041–2060) at areal and Dir, while Kalam, Malam Jabba, and Saidu Sharif showed a 1°C rise. For precipitation, an approximately 12% increase in annual maximum (areal) and seasonal precipitation (summer and autumn) was seen under all scenarios except RCP 4.5 in which there was a 20% and 32% increase in summer and autumn, respectively. The performance of SDSM in simulating maximum temperature and precipitation was satisfactory.

Extreme temperature trend and return period mapping in a changing climate in Upper Tekeze river basin, Northern Ethiopia

There is a consensus that global warming leads to the frequent occurrence of extreme temperature events that significantly impact human life, water resources, agricultural production, and ecosystems. However, the magnitudes of projected temperatures and the probabilities of extreme event occurrences are still deficient for robust planning and early warning efforts at major river basins. This study aimed to analyze extreme temperature trends and mapping return periods in a changing climate for the case of the upper Tekeze river basin, Northern Ethiopia. The future temperature is projected based on global climate models (GCMs) under RCP 4.5 and RCP 8.5 scenarios. The annual extreme temperatures were obtained from the daily temperature data series for the 12 stations by applying the block maxima series method. The non-parametric Mann-Kendall (M-K) statistical test was used for trend detection. The inverse distance weighted (IDW) method has been used to interpolate the point data and reveal the spatial patterns of the extreme temperature of the 50-year return period. The result shows the future extreme temperature will rise by 0.6–2.2 °C. Moreover, the future extreme temperature revealed a significant increasing trend in most of the stations (around 66.3%). The spatial extent of the 50-year return period of the extreme temperature map showed that in the future (2022–2051), the extreme temperature will be increased by 0.2–3.4 °C compared to the baseline period (1980–2009). The extreme temperature trend and return period mapping under climate change will help better local plans and fine-tune mitigation and adaptation measures.

Spatio-Temporal Trends of Precipitation and Temperature Extremes across the North-East Region of Côte d’Ivoire over the Period 1981–2020

The northeast region of Côte d’Ivoire, where agriculture is the main economic activity, is potentially vulnerable to extreme climatic conditions. This study aims to make a comprehensive spatio-temporal analysis of trends in extreme indices related to precipitation and temperature for the Zanzan region of Côte d’Ivoire over the period of 1981–2020. The statistical significance of the calculated trends was assessed using the non-parametric Mann–Kendall test, while Sen’s slope estimation was used to define the amount of change. For extreme precipitations, the results showed a decreasing trend in annual total precipitations estimated at 112.37 mm and in daily precipitations intensity indices. Furthermore, the consecutive dry days’ index showed an increasing trend estimated at 18.67 days. Unlike the trends in precipitation extremes, which showed statistically non-significant trends, the trends in temperature extremes were mostly significant over the entire study area. The cold spells indices all show decreasing trends, while the warm spells show increasing trends. Drawing inferences from the results, it becomes clear that the study area may be threatened by food insecurity and water scarcity. The results are aimed to support climate adaptation efforts and policy intervention in the region.

Temporal-Spatial Characteristics and Future Changes of Temperature Extremes in Longtan Watershed Based on Multiple Indices

Global warming and the intensification of extreme temperature events have been major issues around the world in recent decades. Understanding changes in temperature extremes is critical to assessing and responding to the risks associated with regional temperature change. This paper takes the Longtan watershed as the research object, and 11 extreme temperature indices were calculated based on the meteorological observation data from 1959 to 2017. The Mann-Kendall trend mutation test, Empirical Orthogonal Function, and other methods were used to explore the spatial and temporal distribution characteristics of temperature extremes. Meanwhile, the simulation effects of temperature were analyzed based on 11 CMIP5 climate models, and the extreme temperature change in 2021–2050 under the high emission scenario RCP8.5 and low emission scenario RCP4.5 was estimated. The main results are as follows: both the warm-related indices and the extreme minimum temperature show an increasing trend. The cold-related frequency indices all show a decreasing trend. The spatial distribution of most temperature extremes increases or decreases from southwest to northeast, and the fluctuation is obvious with the alternation of positive and negative positions of the time. In the next 30 years, compared with the reference period 1961–1990, under the RCP4.5, the multiyear average of the Extreme Tmax and the multiyear average of the Extreme Tmin increase by 2.1°C and 0.4°C, respectively, and by 2.0°C and 0.3°C under the RCP8.5. Overall, the frequency of extreme cold events decreases, and the frequency of extreme warm events increases. There is a warming trend in temperature extremes.

Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude

The modeling of spatiotemporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate and assess the environmental, societal, economic and health-related risks they entail. Here, we study annual temperature maxima over Southern Europe using a century-spanning dataset observed at 44 monitoring stations. Extending the spectral representation of max-stable processes, our modeling framework relies on a novel construction of max-infinitely divisible processes which include covariates to capture spatiotemporal nonstationarities. Our new model keeps a popular max-stable process on the boundary of the parameter space, while flexibly capturing weakening extremal dependence at increasing quantile levels and asymptotic independence. This is achieved by linking the overall magnitude of a spatial event to its spatial correlation range in such a way that more extreme events become less spatially dependent, thus more localized. Our model reveals salient features of the spatiotemporal variability of European temperature extremes, and it clearly outperforms natural alternative models. Results show that the spatial extent of heatwaves is smaller for more severe events at higher elevations and that recent heatwaves are moderately wider. Our probabilistic assessment of the 2019 annual maxima confirms the severity of the 2019 heatwaves both spatially and at individual sites, especially when compared to climatic conditions prevailing in 1950–1975. Our results could be exploited in practice to understand the spatiotemporal dynamics, severity and frequency of extreme heatwaves and to design suitable region-specific mitigation measures.

Agricultural management effects on mean and extreme temperature trends

Abstract. Understanding and quantifying land management impacts on local climate is important for distinguishing between the effects of land management and large-scale climate forcings. This study for the first time explicitly considers the radiative forcing resulting from realistic land management and offers new insights into the local land surface response to land management. Regression-based trend analysis is applied to observations and present-day ensemble simulations with the Community Earth System Model (CESM) version 1.2.2 to assess the impact of irrigation and conservation agriculture (CA) on warming trends using an approach that is less sensitive to temperature extremes. At the regional scale, an irrigation- and CA-induced acceleration of the annual mean near-surface air temperature (T2m) warming trends and the annual maximum daytime temperature (TXx) warming trends were evident. Estimation of the impact of irrigation and CA on the spatial average of the warming trends indicated that irrigation and CA have a pulse cooling effect on T2m and TXx, after which the warming trends increase at a greater rate than the control simulations. This differed at the local (subgrid) scale under irrigation where surface temperature cooling and the dampening of warming trends were both evident. As the local surface warming trends, in contrast to regional trends, do not account for atmospheric (water vapour) feedbacks, their dampening confirms the importance of atmospheric feedbacks (water vapour forcing) in explaining the enhanced regional trends. At the land surface, the positive radiative forcing signal arising from enhanced atmospheric water vapour is too weak to offset the local cooling from the irrigation-induced increase in the evaporative fraction. Our results underline that agricultural management has complex and non-negligible impacts on the local climate and highlight the need to evaluate the representation of land management in global climate models using climate models of higher resolution.

Climate change impact on extreme value and its frequency distribution function in a karst basin, Southwest China

Abstract Studying extreme meteorology and its frequency under climate change is helpful to guide flood and drought control. The original achievements and objective of this study are to further contribute to the literature on how to analyze the impact of climate change on extreme rainfall and extreme temperature more reasonably and comprehensively for a karst basin. The Mann–Kendall method, Heuristic segmentation method, cross-wavelet analysis method, generalized extreme value (GEV) model, and generalized Pareto distribution (GPD) model were applied in this paper. The 55-year (1963–2017) extreme rainfall and temperature data recorded in the Chengbi River Basin were applied. The results show that extreme rainfall showed a downward trend (−0.169 and −8.735 mm/10a), while the trends of extreme temperatures were not obvious (Sen's slope estimate is 0). The mutation points range from 1981 to 2002 and the mutation point of extreme rainfall series is earlier than that of extreme temperatures. Compared with the GEV model, the parameters of the GPD model show a smaller variation before and after climate change, and the extreme meteorology values corresponding to the same recurrence period show a decreasing trend after climate change. The performance of GEV and GPD models after climate change is generally more fit than that before climate change.