A B S T R A C T Temperature is one of the climate elements that has fluctuated a lot over time. When these fluctuations increase and decrease more than normal and are placed in the upper and lower regions of the statistical distribution, if continued, it can lead to the creation of heating and cooling waves. The purpose of this study is to analyze the temporal and spatial changes in heating and cooling waves in Iran during a period of 50 years. For this purpose, the Temperature of 663 synoptic stations from 1962 to 2004 was obtained from the Esfazari database. Then, in order to complete this database, the daily Temperature from 2004 to 2011 was obtained from the Meteorological Organization of the country and added to the aforementioned database. In order to perform calculations and draw maps, Matlab, grads and Surfer software have been used. The results of this study showed that the index of cooling waves and heating waves, while having a direct effect on each other, had an increasing Trend in most of the area of Iran. The statistical distribution of the index of cooling waves is more heterogeneous than that of the index of heating waves. So that the spatial variation coefficient for cold waves is 84.22%. Also, the index of cooling waves has more spatial variability. The highest common diffraction of the index of heating and cooling waves has been seen in the northwest, east and along the Zagros mountains. Analysis of the indexes Trends show that heat waves have intensified in 65.8% of Iran and the intensity of cold waves has decreased in 48.5% of Iran Extended Abstract Introduction Temperature is one of the major climatic variables, which it has a direct impact on different aspects of human life. It plays an essential role in the growth of crops and is considered a key driver of the biological system(Reicosky et al, 1988). It is associated with several types of extremes, for example, heat and cold waves which caused human societies maximum damage. Past occurrences of heat waves hitherto had significant impacts on several aspects of society. Have increased Mortality and morbidity. Ecosystems can be affected, as well as increased pressure on infrastructures that support society, such as water, transportation, and energy(Dewce, 2016). The long-term change of extreme Temperatures has a key role in climatic change. The form of statistical distribution and the variability of mean values and also extreme event indicate a change in the region. It can be a small relative change in the mean as a result of a large change in the probability of extreme occurrence. Also, the variation in Temperature data variance is significantly more important than the mean, for assessing the extreme occurrence of climate(Toreti and Desiato, 2008). The average surface Temperature has increased the world between 0.56 and 0.92 ° C over the past 100 years(IPCC, 2007). Meanwhile, it was in the Middle East, the average daily Temperature increased by 0.4-0.5 ° C in decades(Kostopoulou et al, 2014; Tanarhte et al, 2012). Considering that not many studies have been done in the field of spatio-temporal Variations of the heating and cooling waves thresholds in Iran, in this study, the spatio-temporal Variations of the heating and cooling waves thresholds in Iran during 50 years were examined and analyzed.   Methodology The daily Temperature from the beginning of the year 21/03/1967 to 19/05/2005 was obtained from the Esfazari database prepared by Dr. Masoudian at the University of Isfahan. In order to increase the time resolution of the mentioned database, the daily Temperature of observations from 05/21/2005 to 05/12/2012 has been added to the mentioned database using the same method, and the exact spatial resolution (15 x 15 km) is used as a database. Threshold indices of heating waves are the average numbers between the 95th and 99th percentiles, that is, the extreme hot threshold to the limit of excessively extreme hot. For extreme cool, from the 5th percentile down to zero is used. Of course, a condition was added to these thresholds, which is that these thresholds must be repeated two days in a row. These thresholds were extracted for each day in the 50 years of the study period and used as the original database. In order to analyze the relationship between cooling and heating waves, Pearson's correlation coefficient was used and regression was used to analyze the Trend.   Results and discussion The average of cold waves was 5.26 ° C and for the heat waves is 30.20° C. Generally, if the Temperature is upper or lower than this threshold, it is considered as hot or cold Temperatures. A comparison of the median, mode, and average of cold waves with heat waves shows that the distribution is more heterogeneous for cold waves and its CV is 84.22%. In southern Iran, the average threshold heat waves are higher. This situation can be caused by the effects of subtropical high-pressure radiation, low latitude, and proximity to the sea. Though the threshold is higher in these areas, fewer fluctuations and changes are seen in the area. Heights moderate the Temperature so they pose a minimum threshold for heat waves i.e. an iso-threshold of 25 ° C is consistent along the Zagros mountain chains, but in the west and east of Zagros Mountains, the threshold of heat waves is increased. Heat waves have increased in most areas of the country. So nearly 85 percent of the Iran has been an increasing Trend, of which 65.8 percent is statistically significant at the 95% confidence level. Still, more areas of the country (60 percent) have a Trend between 0.00828 and 0.00161. As can be seen, only 15% of the land area (including the southwest and northwest of the Country) had decreased heat waves. Cold waves, in most parts of the country, have a Positive Trend. However, about 25 percent of the study area's cold waves have a negative Trend. they are located in areas higher than Latitude 30°. The largest decline of the wave's Trend along the country is highlands. Nowadays, most of the country, has a Trend between 0.01494 and 0.00828 ° C, respectively. Conclusion Common changes and effects of heat and cold waves had a direct relationship in many parts of the country. It is remarkable common variance in the East reached 55 percent, according to statistical significance. In some areas of the northwest and southwest, which have been impressive heights, the common variance is 40 percent. This common variance in mountains area has been high values. Investigation of heat waves Trend shows that 65.8% of Iran significant positive Trend and 7.1% significant negative Trend. Also, the cold waves Trend has indicated a 48.5% significant positive Trend and a 10.8% significant negative Trend. Climate change and global warming have changed the frequency and severity of Temperature extremes. The present study, by examining the number of warm waves, concluded that the warm waves have increased in magnitude in 65.8% of the Iran zone. Also, the study of the cold waves Trend showed that 48.5 percent of Iran had a positive Trend, which means that the amount of Temperature in the cold waves increased In other words, the severity of the cold has been reduced And only 10.8 percent of Iran had a negative cold wave Trend And it shows the intensity of these waves is reduced.   Funding There is no funding support.   Authors’ Contribution The authors contributed equally to the conceptualization and writing of the article. All of the authors approthe contenttent of the manuscript and agreed on all aspects of the work declaration of competing interest none.   Conflict of Interest The authors declared no conflict of interest.   Acknowledgments  We are grateful to all the scientific consultants of this paper.

Investigation of the Temporal and Spatial Variation of Maximum Soil Temperature in Iran Extended Abstract Introduction The study of soil Temperature in different depths of soil is important in climatology, hydrology, agrometeorology and water resource management. Different depths has a different temporal and spatial soil Temperature variation. It represents the regional ground Temperature regime. Furthermore, due to its rapid response to environmental changes, soil Temperature is one of the most important indicators of climate change. The increase in soil Temperature because of global warming can promotes disasters such as drought by increasing the water demand of agricultural products during the plant growth period. The increase in soil Temperature also have a various consequences, include increasing evaporation from the soil surface, soil salinity in susceptible areas, which can lead to a decrease in soil yield and failure in plant growth. Therefore, knowledge of soil Temperature changes in different environments is very important in climate studies. The aim of the current research is to analyze the spatial and temporal variations of soil Temperature at different depths from five to 30cm of the ground and to investigate the existence of any kind of increasing or decreasing Trend at different climates of Iran. Methodology Hourly soil Temperature data (depths of 5, 10, 20 and 30 cm) were used in this research for the period of 1998-2017. The soil depth Temperature is measured three times a day at 6: 30 am, 12: 30 pm, and 6: 30 pm local time (3, 9, and 3 p. m. UTC). These data have been received for 150 synoptic stations of Iran on a daily basis from the Iran Meteorological Organization (IRIMO). IRIMO monitored the quality of soil Temperature for data entry, data recording, and data reformatting errors. Data availability, discrepancies, errors, and outliers were identified during the second stage. At the first step, temporal coefficient of variation were calculated for available soil Temperature time series from five to 30 cm depths of each station. For this purpose, the average of three daily measurements of soil Temperature was calculated and then the temporal coefficient of variation was obtained. In the next step, Trend analysis of soil Temperature has been investigated using the non-parametric Mann-Kendal test. The Trend slope was calculated using Sen’s slope for each station in seasonal time scale. Trend analysis has been done for all three observations of the day. Results and Discussion The studied stations show significant spatial patterns in the temporal variability of soil Temperature. In all four investigated depths, from five to 30 cm, the northwest parts of Iran, and some parts of Zagros and Alborz mountain ranges have high temporal coefficient of variation. In contrast, the stations located on the southern coasts and southern islands had the lowest temporal variability. In warm and cold seasons (summer and late autumn to mid-winter), the spatial changes of soil Temperature at different depths are lower than spring and early autumn. However, in the warm period of the year, the soil Temperature experiences lower spatial variations at different depths. Spring and autumn seasons, as the transition period from cold to warm and warm to cold seasons, show the most spatial Temperature variations in Iran. Detected Trends do not have significant differences among the three observations of the day. Soil Temperature Trend analysis at different depths showed positive values for two seasons of summer and winter over most of the stations throughout Iran. Extreme Trends are more frequent in the summertime of Zagros and Alborz mountainous regions, while in the winter season the stations located at the southern latitudes of Iran have experienced the most positive Trends. In the summer season, higher Trends with 99% confidence are more frequent in the mountainous areas. These positive Trends in soil Temperature have occurred in all studied depths. The negative Trend at different depths is a distinct feature of the autumn season, which is significantly more prevalent than other seasons throughout Iran. The analysis of soil Temperature Trends in different depths shows that values above 1 degree Celsius often occur in 5 to 20 cm deeps. The increasing Trend of soil Temperature in winter shows a greater spatial expansion, which is indicate increasing annual minimum soil Temperatures and the increasing Trend of Iran's soil Temperature. Keywords: Soil Temperature, Spatiotemporal Variations, Man-Kendal Test, Sen's Slope, Iran

Climate data series usually contain artificial shifts due to inevitable changes in observing instrument or observer, location, environment and observing practices/procedures taking place during the period of data collection. Data discontinuities also arise from the continuously evolving technology of climate monitoring. It is important to detect artificial changepoints in climate data series, because these artificial changes could considerably bias the results of climate Trends and variability analysis. Thus, corrections and homogenization of climate data are imperative for the assessment of observed climate Trends.In this study homogenization of mean monthly Temperature was assessed for 33 synoptic stations in Iran using PMFred algorithm and also linear Trend estimates were obtained using this algorithm. The p value of the linear Trend was determined by the t-test statistic of the slope parameter. The p value is the probability for an estimated positive Trend to be greater than zero, or for an estimated negative Trend to be smaller than zero. The probability for the estimated Trend to be within these intervals is 95%. Linear Trend was estimated for raw and homogenized data in order to evaluation of homogenization effect on Trend analysis. Linear Trend was normalized via half of confidence interval (95% confidence level) so that absolute value of significant Trend (at this confidence level) would be greater than one. Then distribution map of mean monthly Temperature Trend was provided.This study showed that assessment of homogenization using an absolute test can lead to wrong results without the usage of adjacent stations data comparison, if there is no complete and reliable metadata. Because absolute homogenization tests could not realize between natural and artificial shifts and thus should not be used automatically and without subjective qualitative check. Thus adjacent stations data along with metadata (if it exists) was used for the detection of artificial shifts. Mean monthly Temperature data was recognized homogeneous in Tehran, Shiraz, Esfahan, Hamedan-Nojeh, Tabriz, Khoy, Oromieh, Sabzevar, Shahrood, Babolsar and Bandar-Anzali stations and it was recognized inhomogeneous in Zanjan, Saqez, Sanandaj, Kermanshah, Khoram-Abad, Shahrekord, Ahvaz, Abadan, Yazd, Bandar-Abbas, Bam, Kerman, Zahedan, Zabol, Mashhad, Torbat-Heydarieh, Gorgan, Ramsar, Rasht, Qazvin, Birjand and Arak Stations. The results showed that the estimates could be biased by the unaccounted shifts in the series as expected. In the other words, it was observed negative Trend before adjustment in mean monthly Temperature in many stations which have inhomogeneous data, while they showed positive Trend after adjustment (Torbat-Heydarieh, Birjand, Zabol, Gorgan, Bandar-Abbas, Khoram-Abad, Shahrekord, Ahvaz, Zanjan, Rasht, Qazvin, Saqez stations). Estimation of linear Trend for homogenized data revealed that mean monthly Temperature has increased significantly in most stations in Iran. Also, it has not been increased significantly in northwest, except Tabriz station and in Sabzevar- Shahrud to Bandar-Abbas, in a north-south direction. Also a north-south pattern was observed in intensity of increased Trend in Iran. That is Temperature has not increased in the northwest, while it has increased in north to central and southwest of Iran relatively severely (about 0.003 degrees Celsius in each month). It has not increased significantly in east of this region. Also, it has increased in east of Iran severely.

Dew point Temperature is one of the most important atmospheric variables and its long-term changes can lead to changes in the climatic characteristics of an area. In order to spatially analyze the Trend of dew point Temperature changes in Iran, the annual and monthly average data of this variable in 109 synoptic meteorological stations during the statistical period of 1990-2022 have been used. In order to investigate the effect of changes in other atmospheric variables on dew point Temperature, the data of average annual relative humidity, average minimum and average maximum Temperature, and average total annual precipitation have been used. In order to investigate the change process using the Mann-Kendall method and to identify the patterns governing the spatial changes of the dew point Temperature in Iran, the spatial autocorrelation models of Global Moran and Getis-Ord General G statistic have been used. The results of investigating the Trend of dew point Temperature changes show that this variable has an increasing Trend in the areas around the north and South Seas and the northwest region, and a decreasing Trend in the central, eastern and northeastern regions of Iran. Spatial analysis of areas with high dew point Temperature shows a severe cluster pattern, which means that areas with high dew point Temperature are mainly spread in parts of the coasts of the Caspian Sea and many parts of the coasts of the Persian Gulf and Oman Sea, parts of northern Azerbaijan and Ardabil. This shows that the Temperature of the dew point increases as it approaches moisture sources. Also, the spatial analysis of the areas with low dew point Temperature also shows that these types of Temperatures are generally concentrated in a large part of the central areas of the south of Khorasan province and the east and southeast of Iran.

Today, climate change and its anomalies, one of the important issues raised in the world. Detection of changes on Temperature and precipitation as the most important climatic elements, is used as primary evidence of climate change in many parts of the world. This paper investigates the Temperature Trend of Iran of annual, monthly and seasonal scales. For this purpose minimum, average and maximum Temperature data of 38 synoptic stations, over the period of 50 years (1960-2010) with good distribution, were obtained from Iran meteorological organization. Then the Temperature Trend parameters were investigated (studied) by Mann Kendall nonparametric test. The results showed that the general Temperature Trend is increased in most of the stations for different time scales; however this intensity of increase decreases from the minimum to maximum Temperature. On monthly, in June and July, and on seasonal, in summer, the increase of Temperatures are higher than other time. Although the most frequency positive Trend of the maximum and average Temperature observed in the winter, but for a few of the stations was significant. While in warm seasons especially in the summer, the frequency of station with positive significantly Trend is higher. In terms of spatial, the stations that located in the Alborz Mountains, South West and South East of IRAN have experienced higher Temperatures than other areas of the country and in some station such as SHAREKORD, URIMA, KHORRAMABABD AND BANDAR ABASS Trend of Temperature in different scale is mostly decreasing.

Interest in climate changes has significantly increased in recent years due to the important economic and social consequences connected with extreme weather events. In this paper, the variability and extreme event behaviour were studied on the basis of indices of climatic extremes. The indices of Temperature and precipitation extremes in this study were selected from the list of climate change indices recommended by the World Meteorological Organization–Commission for Climatology(WMO–CCL) and the Research Program on Climate Variability and Predictability (CLIVAR). The findings revealed that Fd & ID indices have significant decreasing Trends and the daily minimum &mean Temperature have increasing Trends. In addition, warm extremes indices such as T40, CDD TR ,SU and GDD show a pronounced increasing Trend rather than cold extreme. For example HDD & DTR have decreasing Trends. Indices of extremes precipitation such as SDII,RR20 show low decreasing Trends however the Trend of indices WD, RR5, PER95 is increasing. Symmetric warming in tails of most indices is seen.

Understanding and predicting future climatic conditions and characteristics is crucial due to their implications for various aspects of life. This research aims to forecast Trends in extreme Temperatures in the Hamedan region by employing statistical downscaling of general circulation model data. The LARS statistical downscaling model has been utilized to downscale data from the HadGEM2-ES general circulation model and the coupled CMIP5 model under three emission scenarios (RCP2.5, RCP4.5, RCP8.5). Correlation estimates between the simulated and observed data indicate values exceeding 0.95 for all months. Additionally, the p-values derived from statistical tests based on the model outputs demonstrate an acceptable level of performance in data generation and simulation. Consequently, data from 2011 to 2050 were extracted and analyzed for Trends. To elucidate changes in Trends, the data were examined across three distinct time intervals. The results indicate that in the optimistic scenario (RCP2.5), no significant Trend is observed in the average and minimum Temperatures. In contrast, significant Trends in Temperature data are evident under the RCP4.5 and RCP8.5 scenarios, suggesting that the increase in average minimum Temperatures reflects severe climatic changes, particularly affecting precipitation patterns during the cold season. Furthermore, the analysis of the Trend data reveals a significant increase in average maximum Temperatures on both annual and monthly scales across all three examined scenarios, indicating an imminent environmental crisis.

Regression methods are most applied technique in Trend analysis. The most recent research paid more attention to parametric methods; however, there are many techniques to analyze regression. In this paper, three methods of linear regression have introduced and applied to Tabriz Temperature based on assumpation of its linearity in behavior. So parametric, non-parametric and Byas regression have fitted to annual Temperature of Tabriz. The best regression model have chosen based on t-statistic, precision, deterministic, obviously and residual tests. Based on non-parametric method annual Temperature Trend of Tabriz has on interval of 0.03- 0.04 degree centigrade per year.

Introduction In the pursuit of detecting the Trend and the shift in Trend in hydro-meteorological variables, various statistical methods have been developed and used over the years. Of the two methods commonly used (parametric and non-parametric), the non-parametric method has been favored over parametric methods. Long term Trend analysis can reveal the beginning of the Trend year, Trend changes over time, and abrupt Trend detection in a time-series. It is expected that the findings of this study will bring about more insights on understanding the regional hydrologic behavior over the last several decades in Iran...