Maximum Temperature Series Research Articles

Investigation of Temperature Multifractrality According to Zugspitze Weather Station Data

The main multifractal properties of time series of mean, maximum and minimum daily temperatures are analyzed using the method of multifractal fluctuation analysis. As initial data, we used the results of instrumental temperature observations made at the Zugspitze meteorological station in the period from August 1, 1900 to January 31, 2023. In general, variations in the mean, maximum and minimum daily temperatures demonstrate multifractal behavior, especially for small time scales, up to about 90 days An analysis of the generalized Hurst exponent found that the considered time series have a long-term positive correlation and that the multifractality is weaker with large fluctuations. The singularity spectrum for all time series is truncated to the left, which means that the time series have a multifractal structure that is insensitive to local fluctuations of large values.

On Snowmelt Computations in the Climate Change Context: Comparison of Single Site and Multisite Downscaling of Extreme Daily Temperature Processes

This study focuses on how multisite downscaling procedures can provide a significant improvement in the accuracy of impact assessment results as compared to single-site downscaling techniques. The study compared the accuracy of snowmelt computations given by the multisite multivariate statistical downscaling (MMSD) method and the single site downscaling (SDSM) procedure. Results of an illustrative application using daily maximum (Tmax) and minimum (Tmin) temperature series at seven stations in southern Quebec-Ontario region in Canada indicated that MMSD method was able to reproduce accurately the observed spatial and temporal variabilities of daily extreme temperature processes over different locations. MMSD approach preserved more accurately the interstation correlations of daily Tmax (0.78 and 0.7) and Tmin (0.94 and 0.84) respectively for calibration and validation period than SDSM (0.08 and 0.11 for Tmax; and 0.02 for Tmin). MMSD was found capable to produce more accurate snowmelt estimations than those given by the SDSM.

A comparative evaluation of spatial interpolation techniques for maximum temperature series in the Montreal region, Canada

This study aims to provide a comparative analysis of two of the most used methods of spatial interpolation – Thiessen Polygons (TP) and Inverse Distance Weighting (IDW) with a spatio-temporal approach – Spatio-temporal kriging (STK) on a data series from Canada. The IDW parameter is optimized to obtain the best fitting for the studied series, based on the Root Mean Squared Errors (RMSE) and Mean Absolute Percentage Error (MAPE). The advantages and disadvantages of each algorithm are emphasized. Although TP registered the lowest RMSE and a MAPE, the analysis favors the STK use for modeling Montreal’s maximum temperature series.

Two Centuries of Winter Temperature Variability Inferred from Betula ermanii Ring Widths near the Forests/Tundra Ecotone in the Changbai Mountain, China

In this study, we constructed a ring-width chronology derived from Betula ermanii (BE) near the transitional zone between forests and tundra within the Changbai Mountain (CBM) region. This chronology was established utilizing 55 cores obtained from 30 trees. Our analysis of growth/climate responses underscores the pivotal role of the mean maximum winter temperature in influencing radial growth. Drawing upon these growth/climate associations, we reconstructed the mean maximum temperature series for December of the preceding year through January of the current year for the years 1787 and 2005 CE, employing a standardized chronology. During the calibration period (1960–2005), the reconstructed series exhibited an explained variance of 36%. This reconstruction provides crucial insights into historical temperature fluctuations within the study area. Our findings indicate that year-to-year temperature variations did not manifest synchronously along the altitude gradient of Changbai Mountain. Notably, the response to recent winter warming exhibited disparities with the altitude on Changbai Mountain. Specifically, the higher altitude range (1950–2000 m a.s.l.) displayed a response to warming around 1960, the mid-altitude range (765–1188 m a.s.l.) responded around 1975, and the lowest altitude (650 m a.s.l.) responded by 1977. Consequently, the paleotemperature research outcomes from Changbai Mountain alone may not adequately characterize climate change in this region. We recommend future high-resolution temperature records be obtained through sampling at various altitudes to enhance the comprehensiveness of our understanding.

Homogenizing Monthly Rainfall and Temperature Data Series in Maharashtra & Goa

Annual rainfall and temperature data series of all climate stations in Maharashtra & Goa are statistically tested for data homogeneity. To inspect homogeneity of a station, a two-step approach is followed. First, four homogeneity tests Standard normal homogeneity test, Pettit’s test, Buishand’s range test and Von Neumann ration test at 5% level of significance are used to determine test hypothesis for homogeneity on testing parameters of annual rainfall and temperature. Second, results from all these four tests aggregated together into three different classes as ‘useful’, ‘doubtful’ and ‘suspect’. Here 30 rainfall, 29 maximum and minimum temperature climate stations were tested. The results showed 80% stations as ‘useful’, 7% as ‘suspect’ and 13% as ‘doubtful’ for rainfall, for maximum temperature series these results are 17% as ‘useful’, 7% as ‘suspect’ and 76% as ‘doubtful’, while for minimum temperature series these results are 21% as ‘useful’, 10% as ‘suspect’ and 69% as ‘doubtful’. Further, in this study an attempt is also made to correct the monthly rainfall and temperature data series for homogeneity. Stations categorised as ‘useful’ are used as reference series to remove inhomogeneities from ‘suspect’ and ‘doubtful’ stations. To correct rainfall series ratio’s method is used while for temperature series addition method is used. Correction results showed significant improvement in ‘suspect’ category stations. After correction of inhomogeneous series, the results shows all 100% of rainfall stations and more than 65% of temperature stations are now in ‘useful’ category. The corrected stations may be included in further climate research studies.

Nonparametric tests of extremes of daily maximum temperatures based on the breaking records: A case study in Seoul and Busan during 1961-2022

An observation in a time series is called an upper record if it is greater than all previous observations in the series. Also it is called a lower record than all previous observations in the series. Therefore, the analysis of record-breaking events is of interest in fields such as climatology, economics and sports. As an alternative to the classical extreme value theory, which are based on the fit of generalized extreme value (GEV) distribution, generalized Pareto (GP) distribution and Poisson processes, this work is based on the study of record-breaking events to analyze non-stationarity in the extremes. The contribution of this work is the use of the statistical tools developed by Castillo-Mateo et al. (2023) to assess and analyze the effect of global warming in the extreme and record breaking events using daily maximum temperature series in Seoul and Busan over 1961-2022. These test statistics consider the information based on the four different types of record, that is, forward upper, forward lower, backward upper and backward lower. Also, change point detection is implemented based on records.

Homogenization of daily temperatures using covariates and statistical learning—The case of parallel measurements

AbstractA data driven method based on generalized additive modelling (GAM) has been developed for homogenizing daily minimum and maximum temperature (TN, TX) series using parallel measurements and covariates. The method is applied to two coastal and two inland stations in the Netherlands. Between 1950 and 1972, these stations were relocated from cities to airports, accompanied by parallel measurement of at least 5 years at the old and new sites. Separating these parallel measurements in training and test data, the method compares numerous models involving covariates like the wind vector, cloudiness, specific humidity and sea surface temperature, and selects a model for each station. The resulting models offer an improvement compared to models based on temperature and season only: seasonal dependence is largely replaced by dependence on physical quantities. However, quantitatively, the impact is not large in the cases studied. One of the reasons might be that some covariates have only been measured at specific times not coinciding with the occurrences of the temperature minima or maxima. Additional benefits of the method are robustness and estimation of the sampling error variance of the daily homogenized daily temperature values.

Long-term air temperature trends in North Cyprus

The Eastern Mediterranean region is a major climate change hotspot. The island of Cyprus is likely to face increases in the frequency and intensity of hotter weather conditions and heatwaves in the near future. Studies conducted on the long-term temperature changes in Cyprus are very limited. Here, we present an updated and most detailed assessment of the maximum, minimum, mean and diurnal temperature series in North Cyprus for the period 1975–2021. Data obtained from the meterological stations of North Cyprus have been analysed using Mann–Kendal (MK) test and Theil–Sen slope estimator. Overall the mean minimum temperature trend (Tmin) showed the highest warming rate 0.61 (0.24 ≤ Tmin ≤ 0.99)°C decade−1 followed by the mean temperature trend (Tmean) 0.38 (0.29 ≤ Tmean ≤ 0.50) °C decade−1 and the mean maximum temperature trend (Tmax) 0.28 (0 ≤ Tmax ≤ 0.50) °C decade−1. The magnitude of the warming trend observed in the overall mean minimum temperature of North Cyprus 0.61 °C decade−1, is one of the fastest warming trends reported in the literature. A negative association was detected between the direction of prevailing winds of North Cyprus and the magnitude of increase in the mean temperature trends of the locations with coastal Mediterranean climate, which has pointed out the importance of prevailing winds regarding their cooling effect in coastal areas. The diurnal temperature range trend of North Cyprus indicates an apparent decrease (− 0.33 °C decade−1). The warming impact of urban heat island effect was detected in temperature trends of Nicosia in the Mesaoria plain. The information provided here is invaluable to consider in any climate assessment and adaptation plan in Cyprus. If the current warming trend persists into the future, it will devastatingly impact all sectors and natural systems in the region.

Modelling spatiotemporal tendencies of climate types by Markov chain approach : A case study in Sanliurfa province in the south-eastern of Turkey

Identification of spatiotemporal tendencies of climate types may help water managers mitigate the negative impacts of droughts on water-demanding sectors. The primary objective of this study was to figure out the spatiotemporal tendencies of climatetypes in Sanliurfa province by using Erinc’s aridity index (EDI). To that end, long-term (1965-2018) annual precipitation and average annual maximum temperature series of meteorological stations were obtained and utilized to calculate the EDI series on a yearly basis. The EDI series of each station was divided into three periods, non-overlapping and successive, i.e., P1 (1965-1981), P2 (1982-1999) and P3 (2000-2018). Outliers were detected, andremoved from the EDI series; missing data were completed by regression analysis. The Markov transition probability matrix of the climate classes for the three periods was estimated for each station. Maps of the initial probability vectors and steady-state probabilities for the three periods of each climate class were generated by the inverse distance-weighted technique. Hypsometric curves for each climate class, as well as period, were developed and areal coverage of occurrence probabilities (OP) was determined. Results indicated that, as time progressed, the areal extent of severe-arid and arid climatic classes continued consistently to spread from the south to the north. Areas of semi-arid climate type showed a slight tendency towards the arid-climate type. Construction of large dams in the region could not prevent the shifts in the climate in favour of developing arid zones. The humid climate class is likely to vanish away in the future. Research led us to conclude that the expansion of the aridzone from south to northhas been alarming in terms of the adequacy of water resources. It is strongly recommended that spatiotemporal climate change studies should be periodically conducted in tandem with forest management practices for the region.

Regionalisation of heat waves in southern South America

This study describes the climatological characteristics of regional heat waves (HWs) over southern South America (SSA) for the warm seasons (October–March) of 1979–2018 based on daily maximum temperature series from 131 weather stations. Clustering of stations with high co-occurrence of simultaneous HW days is employed to identify regional HW events over five homogeneous regions: northern, central-eastern and southern SSA regions, central Argentina, and central Chile. When all regions are considered, we find a mean frequency of ∼4 HWs per year. Transitional regions (northern SSA, central-eastern SSA and central Argentina) are characterised by longer, albeit less intense, HWs than the southernmost region (southern SSA), whereas central Chile events display the lowest duration, intensity and extension. By aggregating these single HW attributes into a combined severity index, a ranking of historical HWs has been obtained, with the March 1980 event standing as the most severe one of SSA. The assessment of long-term changes reveals significant increases in the frequency of regional HW days over central Argentina and central Chile only. Trends in HW characteristics are also region dependent, and the southernmost region is the only one where HW severity has increased significantly.We report similarities and differences in the synoptic circulation patterns associated with regional HW events. Southern SSA HWs have the most distinctive signatures, related to extratropical high-pressure systems blocking the westerly flow. In the remaining regions, HWs are associated with anomalies in the South Atlantic (northern SSA, central-eastern SSA and central Argentina) or South Pacific (central Chile) High, and the intensification of the northerly low-level flow by regional thermal lows and South American Low Level Jet events. Regional HWs often migrate from northern to central-eastern SSA and central Argentina, following the displacement/intensification of the South Atlantic High, which partially explains the similarity of their associated patterns.

The observed features and some possible reasons of annual temperature extremes over Beijing‐Tianjin‐Hebei region for a century long‐term based on newly constructed daily observations

AbstractThe observation facts of trend and variability are estimated for a century‐long timescale, as well as some possible attributions affecting temperature extremes over Beijing‐Tianjin‐Hebei region. In this paper, the new constructed daily maximum and minimum temperature series during the last 100 years in situ stations of Beijing, Tianjin and Baoding city are used, and two 30‐year baseline climatology of 1961–1990 and 1980–2010 are both used in calculating extreme temperature events. The results indicate that there are long‐lasting, frequent and serious extreme warming over Beijing‐Tianjin‐Hebei region, especially for extreme low temperature, with larger declining trends (at 5% significance) from 1981 to 2010 baseline than those from 1961 to 1990 baseline. And from a statistical perspective, El Niño decay years and surface sea temperature (SST) anomalies at some typical ocean regions of the Arabian Sea and the Bengal Bay on the northern Indian Ocean, the Western Pacific and the Mid Atlantic, Southern Indian Ocean and around 45°–85°E, 68°–82°N on the southwest Arctic Ocean can be used as the warning signals denoting extreme warming over Beijing‐Tianjin‐Hebei region. Additionally, urbanization has a significant (at 5% significance) effect on extreme warming over Beijing‐Tianjin‐Hebei region since 1979, with 24.3% warming contribution for annual minimum temperature.

On the Regional Temperature Series Evolution in the South-Eastern Part of Romania

In the context of reported climate variations in different regions of the world, this work investigates the evolution of the temperature series in the Dobrogea region, Romania, using the maximum, average, and minimum annual temperature series from 1965 to 2005. The Mann–Kendall test and Sen’s slope emphasized increasing trends of nine (out of ten) minimum temperature series (nine of them at significance levels less than or equal to 0.05, and two at 0.1), three average temperature series (at a significance level of 0.1), and five maximum temperature series (at significance levels less than or equal to 0.05). The selection of the representative series at the regional scale, called the ‘Regional series’, was performed using two algorithms proposed by the author that are easy to employ, even by individuals without deep knowledge in the field. The first (called MPPM) was initially introduced for evaluating the ‘Regional precipitation series’, and the second is a version of MPPM based on clustering the data series. Comparisons with the series from the ROCADA database were performed to prove the algorithms’ performances. The best results were obtained by running the second algorithm with two clusters, for the minimum and maximum temperature series, and with three clusters, for the average temperature series. In comparison with the initial data series, the average MAEs were, respectively, 1.39, 0.37, and 0.84 for the minimum, average, and maximum series, and the corresponding average MSEs were, respectively, 1.49, 0.41, and 0.93. Comparison of the ‘Regional series’ with the series from ROCADA led to a decrease in the modeling errors, with the best ones corresponding to the average ‘Regional series’—MAE = 0.36 and average MSE = 0.25.

Bayesian Variable Selection in Generalized Extreme Value Regression: Modeling Annual Maximum Temperature

In many applications, interest focuses on assessing relationships between covariates and the extremes of the distribution of a continuous response. For example, in climate studies, a usual approach to assess climate change has been based on the analysis of annual maximum data. Using the generalized extreme value (GEV) distribution, we can model trends in the annual maximum temperature using the high number of available atmospheric covariates. However, there is typically uncertainty in which of the many candidate covariates should be included. Bayesian methods for variable selection are very useful to identify important covariates. However, such methods are currently very limited for moderately high dimensional variable selection in GEV regression. We propose a Bayesian method for variable selection based on a stochastic search variable selection (SSVS) algorithm proposed for posterior computation. The method is applied to the selection of atmospheric covariates in annual maximum temperature series in three Spanish stations.

Analysis of relationship between cooling capabilities of a cooler and variation of branch geometry in cascades

A reliability-oriented model of relation of thermoelement branch geometry (ratio of height to cross-sectional area) to cooling capabilities of two-cascade thermoelectric devices of various designs has been developed. Researches have been carried out for the maximum temperature difference regime and series connection of thermoelement branches in cascades. Analytical expressions have been obtained which allow determining maximum cooling capabilities of a thermoelectric two-cascade device, intensity of failures and probability of no-failure operation depending on the ratio of the number of thermoelements in cascades. A relationship between the ratio of the number of thermoelements to the intermediate temperature and relative operating current in cascades is analyzed. Possibility of increasing of maximum temperature difference by choosing of optimal geometry of thermoelements branches in cascades has been grounded. The ratio of length to section area of elements of the first stage more than the ratio of length to section area of the second stage that differs from traditional equality of these relations at the set operational current. The proposed approach makes it possible to estimate the maximum temperature drop and predict the reliability indices of two-cascade thermoelectric coolers of various designs for different operating conditions and to conduct optimized design of thermoelectric systems for ensuring thermal regimes of electronic equipment.

Long-term changes in surface air temperature over the Chinese mainland during 1901-2020

The magnitude of long-term surface climate warming over some regions, such as the Chinese mainland, is still uncertain due to the lack of observational data early in the 20th century. In this study, the monthly data series of the average, maximum, and minimum temperatures in the Chinese mainland during 1901-2020 were constructed based on the daily surface air temperature observations from 60 stations across the country, and the characteristics of the average, maximum, and minimum temperature, and diurnal temperature range (DTR) changes were analyzed. Results show that (1) regional average annual mean temperature in the Chinese mainland rose by 0.14°C per decade, maximum temperature rose by 0.07°C per decade, minimum temperature rose by 0.19°C per decade, and DTR decreased by 0.13°C per decade. All these trends are statistically significant (p < 0.01); (2) the largest annual mean maximum temperature increase occurred in spring, followed by winter and autumn/summer, and the largest annual mean minimum temperature increase was in winter and spring, followed by autumn and summer; (3) annual mean DTR decreased significantly at a rate of -0.08, -0.12, -0.12, and -0.13°C per decade (p < 0.01) in spring, summer, autumn, and winter, respectively; (4) the stations with drops in maximum temperature were mainly in Central China, southern North China, the southeastern coastal areas, and the middle and lower reaches of the Yangtze River, and the stations with significant increases in minimum temperature were located in North China, Northeast China, and Northwest China; (5) the areas with the fastest dropping DTR were mainly located in Northeast China and North China. The maximum and minimum temperature series for China based on climate anomalies are comparable to those based on other currently available datasets.

The Determination of the Most Appropriate Probability Distribution Models for the Meteorological Variables

Every component of the hydrological cycle is essential for controlling water supplies and assessing the potential catastrophic events like floods and droughts. The variables of hydrological system are unexpected and unique to each place. In this paper, the most crucial variables including precipitation, temperature, relative humidity, and evaporation are examined for Ankara province. For meteorological parameters, the Lognormal, Log-logistic, Gamma, Weibull, Normal, and Gumbel models are used to find the best suitable distributions. Kolmogorov-Smirnov, Cramers-von Mises, Akaike's Information Criterion, Bayesian Information Criterion, Anderson-Darling, and Maximum Loglikelihood methods are utilized to test these models. Results shows that there is a distinct distribution model for each parameter. In particular, it has been determined that the Gumbel distribution is a better model for annual total precipitation, whereas the Normal distribution is a better model for annual minimum temperature. At stations 17130 and 17664, the gamma distribution is observed to be the best fit distribution at annual total precipitation, but station 17128 is found to be the most appropriate Log-logistic and normal distribution. Stations 17128, 17130, and 17664 for annual maximum temperature series are fitted with the Normal, Log-logistic, and Lognormal, respectively. Gamma is found to be the best fit when analyzing annual mean temperature for stations 17128 and 17130, whereas Lognormal is selected for station 17664. It is expected that these results will contribute to the planning of water resources projects in the region.

Analysis of meteorological parameter changes using Mann-Kendall statistical tests in Indian Punjab

Groundwater in Central Punjab is declining at an alarming rate of one meter annually in last two decades and likely to be exhausted up to 300 meter depth in next 2-3 decades as per recent report published in 2018 based upon exhaustive analysis done till 2017 by the relevant agencies (Anonymous, 2018). All the blocks in this zone are over exploited. Rainfall and temperature are predominant meteorological parameters which influence irrigation demand which in turn influences ground water withdrawal; which ultimately influence groundwater depth in the region. Area under rice is increasing due to high returns. So, the trend analysis of these parameters is of paramount importance for sustainability of agriculture in this region. The aim of the present investigation was to study annual trend of precipitation, maximum and minimum temperature and rainfall for 10 locations in Central Punjab during 1998–2019. The effect of rainfall and rice area on groundwater depth has been also studied. To achieve this non-parametric Mann-Kendall and Sen's applied to the data series of annual rainfalls, annual maximum and minimum temperature of the ten selected stations. The nonparametric statistical tests applied to series of annual Tmax revealed an increasing trend in all of the locations of the study area. According to results by Sen’s slope estimator, the increasing trend in annual precipitation series was detected at the Tarantaran, Kapurthala,Ludhiana, Moga, Barnala and Jalandhar, while other stations had negative or no trends.

Understanding Future Climate in the Upper Awash Basin (UASB) with Selected Climate Model Outputs under CMIP6

Climate change makes the climate system of a given region unpredictable and increases the risk of water-related problems. GCMs (global climate models) help in understanding future climate conditions over a given region. In this study, 12 GCMs from the CMIP6 (coupled model intercomparison project six) were evaluated and ranked based on their abilities to describe the historical observed series. The ensemble mean of bias-adjusted best five models of average annual precipitation showed an increment with an uncertainty range of (2.0–11.9) and change in the mean of 6.4% for SSP2-4.5 and (6.1–16.1) 10.6% for SSP5-8.5 in 2040–2069 relative to the historical period. Similarly, for 2070–2099, increments of (2.2–15.0) 7.9% and (11.8–29.4) 19.7% were predicted for the two scenarios, respectively. The average annual maximum temperature series showed increments of (1.3–2.0) 1.6 °C for SSP2-4.5 and (1.7–2.3) 2.0 °C for SSP5-8.5 in 2040–2069. At the same time, increments of (1.7–2.3) 2.0 °C and (2.8–3.2) 3.0 °C were predicted for 2070–2099. Furthermore, it was predicted that the average annual minimum temperature series will have increments of (1.6–2.3) 2.0 °C and (2.2–2.9) 2.5 °C for 2040–2069 and (2.1–2.7) 2.4 °C and (3.7–4.2) 4.0 °C for 2070–2099 for the two scenarios, respectively. An increase in precipitation with increased land degradation in the sub-basin results in a higher risk of flood events in the future. Improved soil and water conservation practices may minimize the adverse impacts of future climate change on the loss of agricultural productivity.

Trend analysis of observed precipitation, temperature, and streamflow for Hadejia-Nguru wetlands catchment, Nigeria

This study investigated trends in hydro-climate data (precipitation, maximum/minimum temperatures, and streamflow) for the period 1980–2016 around a semi-arid Hadejia-Nguru wetlands (HNWs) in Northern Nigeria. Four meteorological stations (Bauchi, Kano, Hadejia, and Nguru) and three streamflow gauge statins (Hadejia, Katagum, and Gashua) were used to cover the HNWs’ catchment. The data was checked for normality using Anderson–Darling and Shapiro–Wilk tests. Pettit’s test was used to check for homogeneity in the data series, and absolute homogenization, using RHtestsV4 software, was applied to homogenize and pre-whiten the data series. Trend analysis was carried out using modified Mann–Kendall and Sen’s slope tests. Results revealed increasing positive trends at all the stations for monthly minimum, average annual minimum, average annual maximum, and annual average temperature data series, while monthly maximum temperature series of Bauchi and Kano stations indicated the presence of an increasing trend with magnitudes 0.0035 °C and 0.0019 °C; and no trend was detected for Hadejia and Nguru stations. For monthly precipitation, no trends were detected at all stations. However, an increasing trend was detected at Bauchi, Hadejia, and Nguru stations for mean annual precipitation with magnitude 7.7960 mm/year, 8.1766 mm/year, and 5.7214 mm/year, respectively. A decreasing trend was detected for monthly and annual streamflow series at Hadejia gauge station with magnitude − 0.0115 m3/s/month and − 3.7037 m3/s/year, respectively; and no trend was detected for monthly and annual streamflow series at Katagum and Gashua gauge stations. The trend analysis may be helpful for planning and management of water resources in the HNWs.

A statistical approach to multisite downscaling of daily extreme temperature series: A case study using data in Bangladesh

Downscaling techniques are required to describe the linkages between Global Climate Model outputs at coarse-grid resolutions to surface hydrologic variables at relevant finer scales for climate change impact and adaptation studies. In particular, several statistical methods have been proposed in many previous studies for downscaling of extreme temperature series for a single local site without taking into account the observed spatial dependence of these series between different locations. The present study proposes therefore an improved statistical approach to downscaling of daily maximum (Tmax) and minimum (Tmin) temperature series located at many different sites concurrently. This new multisite multivariate statistical downscaling (MMSD) method was based on a combination of the modeling of the linkages between local daily temperature extremes and global climate predictors by a multiple linear regression model; and the modeling of its stochastic components by the combined singular value decomposition and multivariate autoregressive (SVD-MAR) model to represent more effectively and more accurately the space-time variabilities of these extreme daily temperature series. Results of an illustrative application using daily extreme temperature data from a network of four weather stations in Bangladesh and two different NCEP/NCAR reanalysis datasets have indicated the effectiveness and accuracy of the proposed approach. In particular, this new approach was found to be able to reproduce accurately the basic statistical properties of the Tmax and Tmin at a single site as well as the spatial variability of temperature extremes between different locations. In addition, it has been demonstrated that the proposed method can produce better results than those given by the widely-used single-site downscaling SDSM procedure, especially in preserving the observed inter-site correlations.