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Although it is questionable whether there is climate change, but almost all climatologists agree global warming is a problem and that climate risk. Because of this, the research ahead is done for the detection of global warming on minimum temperatures, monthly and periodic (hot and cold) as well. For this study, two groups of data, temperature data of 17 synoptic stations and corresponding amounts of data in global temperature anomalies were figured out over 60 years period of time (1951 to 2010). Goals, the Pearson correlation method for detecting relationships between data's, linear and polynomial regression for trend analysis time series data, To illustrate the correlation between the spatial distribution of temperature data with global warming stations nationwide Geostatistical model Finally, non-parametric test for detecting significant temperature change Man - Kendall were used. According to the results, all studies stations apart from Urmia and Khorramabad experience increasing trend in the average of temperature. The most influence over global warming observed from April to October is the month of the summer than other months of the relationship that has a significant high than the average summer temperature is going up. This process in the analysis of time-series and temperature trends has been quite evident. Change in trend occurred been a significant in most months and changes in the average temperature trend has been confirmed. The results obtained from the analysis period (hot and cold) temperatures average, indicating a strong relation to the heat period than the cold periods. The change in temperature trend occurred in both studies period, According to the results obtained are quite significant.

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Climate can easily be considered as the most important factor affecting human life and the life of human societies. Perhaps the importance of the climate in human life has never been stressed as much as it is today in macroeconomic-social debates. Nonetheless, even today, many decision-makers of macroeconomic issues are not that cognizant of the importance of this issue. Now, most experts of political, economic, social and environmental issues present climate change as one of the most complex and influential issues in various areas of human life. They also predict that this issue will bring about different problems and consequences for human societies, depending on their economic, social and environmental context, especially in economic terms. Although the issue of climatic fluctuations and their economic impact on human life is not a new issue, the issue has recently taken a different turn. The financial costs incurred by climatic fluctuations will be very heavy and even irreparable for some areas and countries. In total, climate change is expected to impose, either directly or indirectly, significant economic impacts on human societies in various fields – especially agriculture, tourism, energy, human health, labor productivity, employment, economic growth, rising poverty, and increasing immigration, among others. However, the economic impact of climate change will not be equal and alike the world over. In other words, for some regions, climate change can be considered as a geo-economic opportunity and economic advantage, especially in the world economy and economic relations; in contrast, climate change for many regions and developing countries with very hot and dry weather is a geographical and economic crisis and bankruptcy. Due to the importance of the subject, this article tries to study the economic effects of climate change through a descriptive-analytical method. The issue is studied due to the fact that the literature of climate change in the Persian language is scarce; as well, from the climatic and economic point of view, Iran is one of the most vulnerable areas for climate change.

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Land Surface Temperature (LST) is one of the most important parameters of environmental and climatic conditions. Therefore, this study is to investigate the trend change of day and nighttime LST in ​​Iran at a monthly and annual scale. To achieve this goal, the monthly products of MODIS sensor, Aqua satellite for day and nighttime with a spatial resolution of 5 km were obtained from USGS from 2020 through 2003. Then a three-dimensional matrix with dimensions of 216 × 297 × 388, in which 297 × 388, the number of cells and 216 months, was created. Finally, the monthly and annual changes of these two parameters were performed using Mann-Kendall test. Although the average annual trend of day and nighttime LST is increasing, on a monthly basis, different patterns are observed. The LST at nighttime increased significantly in May and September and during the daytime in December, but in June, July and January both day and nighttime temperatures increased. In contrast, both in March and October had a decreased trend. There was also a decreasing trend in nighttime LST in February and daytime in April and November. The results showed that the nighttime LST increased more than the daytime temperature, which led to a decrease in the 24h temperature range. In the spatial dimension, the highest increasing trend was observed in the western and southwestern regions and the highest decreasing trend was observed in the central plateaus and southeastern. Another point is that the water areas undergoing change, such as Lake Urmia and the lakes of Fars province, have experienced a significant increase (decrease) in LST during the day (night). In addition to decreasing the range of 24h temperatures, due to the fact that in the northern and western half of the country the upward trend was more and in the center, east and southeast the decreasing trend of temperature was predominant, the amplitude of spatial temperature gradient also decreased.

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Introduction
The impact of climate change on tourism has been the subject of many research studies. It is for this reason that tourism researchers have continued to explore the relationship between tourism and climate change and further explored response strategies among tourism stakeholders. The aim of this research is the evaluation of climate change and its impacts on thermal comfort and tourism climate of Iran. For this purpose daily maximum and minimum temperature, wind speed and relative humidity data of 91 synoptic stations during a period of 30 years (1987-2017) were used. In this research the (CDFt)[1] model is used to downscaling the GCMs data. ERA5 data was used to evaluate the performance of the downscaling model. The CanESM2 and GFDL_ESM2G daily data were used to draw future landscape changes of thermal comfort (based on the 2.6, 4.5, 8.5 RCPs). Then the thermal comfort for tourists was calculated using PET[2]. The thermal comfort results compared between the present and future. Thermal comfort of Iran was predicted for 2 periods (2021-2040) & (2041-2060). The results are displayed in ten-day classes. The number of stations that have optimal thermal comfort conditions in observation period indicates 0.83% increase compared to the futures perspective. The best conditions of climate comfort is predicted in the 24s and 25s decades and the most undesirable conditions are in the first to fifth and 34 to 36 decades. The most PET decrease is predicted in the northeast of Iran and the least increase of PET are occurred in the northwest. The results indicate an increasing on PET (between 0.06 to 1.56%) at future due to the increase in average temperature.
Materials and methods
The daily variables of maximum and minimum temperature, wind speed and relative humidity of Synoptic stations as well as the ERA5 database in a 30-year period (1987-2017) and GCMs data were used to simulate the PET from 2021 to 2060. The CDFt model was validated using the Pearson correlation coefficient and the Kolmogorov-Smirnov tests along with a climatic RMSE. There are often abrupt changes in the climate time series. The homogeneity of observed data evaluated using the RhtestV4 software package based on the maximal penalized T and F (MPF, MPT) tests.  Data imputation was performed using Sequential K-Nearest Neighbor Method.
Then, the research process was done as follows:

1. The variables of maximum and minimum temperature, relative humidity and wind speed was simulated by CDFt statistical downscaling. The CDFt can be considered as an approach to the Quintile Mapping (QM) method by providing cumulative distribution functions (CDFs). Assuming that the ERA5 data are converted to the cumulative distribution function of the local climate variable as predictand at the desired station. In this study the CDFt software package has been used in R software. Validation of the simulated data was performed by Pearson correlation coefficient, Kolmogorov-Smirnov test and tidal error of the mean square squares.
2. The RayMan model was used to calculate the thermal comfort based on PET index using both the observed and the GCMs downscaled data (CanESM2 & GFDL_ESM2G). This model is able to calculate the effect of short and long wave radiation flux on the human body, which is required in the human energy balance model.

Results and Discussion
After making sure that the CDFt, in general, showed a good performance in downscaling of the variable applied to calculation of climate comfort in the study area. Therefore, it is reliable to project the future thermal comfort of the region under the climate change conditions.Then  the historical period of CanESM2 & GFDL_ESM2G Models were used to show a comparison between PET observations and PET simulations. The results of the thermal comfort verification calculated based on the historical period simulated values ​​in comparison to the observed values ​​are as follows:
There is a high correlation between the time series of the PET observed and PET simulated the historical period of GCM Models. However, a linear and positive relationship was observed for all-time series. According to the Kolmogorov-Smirnov test, the simulated values ​​of all PET time series showed good fit with the observed data at the 0.01 significance level. The calculated value of RMSE test for PET indicates the high performance of the downscaling method in simulation of historical period of GCM Models. The results showed that the CanESM2 model with minor differences performed better than the GFDL_ESM2G model. Incremental changes of physiological equivalent temperature have occurred in higher latitudes and in the high places like Alborz and Zagros. Also the PET decreasing changes are in low latitudes and central parts of Iran.
Therefore, it is expected that more area of Iran will be at the desired threshold of thermal comfort at the future.
The results showed that in the rest of the year, the largest utilitys are in the 24 and 25 th decades. Regardless of the model type towards the Rcp4.5 and Rcp8.5 and the 2041 to 2060 period increasing the values of the Physiological Equivalent Temperature will proceed. Also, the length of comfort decades is increasing in future periods.
Conclusion
This study was performed in order to provide infrastructure for tourism in future periods from 2021 to 2060. For this purpose, the climate comfort of Iran was predicted by GCMs data. Currently, most parts of the Iran are in the range of mild to severe cold stress. It is predicted that wider parts of study area will have optimal comfort conditions in future. Also, the number of optimal comfort decades is increasing compared to the observation period. The results of the climate comfort study showed that mild to severe cold stress conditions are predicted in More than half of this country at different decades of the year. So it will create opportunities and constraints in the future which requires long-term planning and strategies in this area.