Extreme precipitation is considered as a serious hazard, especially in arid and semi-arid regions as they increase the risk of flooding and leave limited time for warning. The aim of this study is to evaluate precipitation of the fifth-generation reanalysis (AgERA5) of the European Centre for Medium-RangeWeather Forecasts (ECMWF) and to investigate the seasonal distribution of extreme precipitation in Iran. In this study, the accuracy of AgERA5 precipitation is evaluated using NRMSE, MBE, and PCC statistics. The error analysis shows that AgERA5 has the highest NRMSE in the humid climate of the northern coasts as well as the rainy regions of Zagros and Northwest of Iran. In contrast, this dataset estimates precipitation in arid and semi-arid regions of Iran more accurately. Three indices, including SDII, RX1day, and R10mm, were used to examine seasonal precipitation. The evaluation of extreme indices shows that the AgERA5 dataset is underestimated R10mm in large parts of the country, and in contrast, the two indices RX1day and SDII are overestimated in most parts of Iran. Like the average precipitation, the maximum error and bias of extreme precipitation are seen on the Southern Caspian Sea coast. The results showed that the maximum one-day precipitation (RX1day) in Iran is 80.5 mm in winter. The maximum daily precipitation intensity (SDII) is observed in southeastern Iran, with 19.2 mm/day in summer. The Southern Caspian Sea coasts show the highest continuity of days with heavy precipitation in all seasons. Despite the fact that this region has the highest number of heavy precipitation days in all seasons, the maximum continuity of heavy precipitation is seen in the high Zagros mountains. Precipitation intensity in all regions of Iran is directly related to altitudes. In this regard, the southern coast of the Caspian Sea is an exception throughout the year.

Temperature is a major variable in the Earth's climate system, which plays an important role in energy exchange interactions between the Earth's surface and the atmosphere. There are various sources for temperature estimation, including ground stations, satellite products, reanalysis datasets, and multi-source weighted-ensemble datasets. Reanalysis datasets are generated by combining different types of observational data for a certain time in numerical weather prediction models and using ground and satellite observations. The purpose of this research is to investigate the performance of the ERA5-Land and AgERA5 reanalysis datasets as well as the MSWX multi-source dataset to determine reliable datasets for estimating temperature in Iran. First, we evaluated the temporal variations of the three datasets against the station data. We used the air temperature from 98 stations for 30 years from 1991 to 2020. Three metrics including Root Mean Square Error, Bias, and Index of agreement were used to evaluate ERA5-Land, AgERA5, and MSWX datasets.Then, considering the seven main climate zones of Iran, the spatiotemporal quality of the annual mean temperature was evaluated in different climate zones. The results showed that all three datasets have less error and bias in the estimation of the minimum temperature of Iran. However, AgERA5 and MSWX significantly showed less error in the estimation of the maximum temperature with RMSE of 1.74℃ and 1.42℃, respectively. On the other hand, the ERA5-Land dataset shows overestimation (5.05℃) and high error values (5.07℃) over the country-averaged. The results showed that the MSWX dataset has a better performance in estimating Iran's temperature with an average bias of 1℃. The interannual variations and decreasing and increasing trends of temperature in three datasets with correlation above 0.86 in all climate zones of Iran show a high consistency with observational data.The RMSE in all three datasets reaches its maximum in the winter season in mountainous climate zones of the country. This may be caused by snow-albedo feedback in mountainous climate zones. The findings showed that performing bias correction and downscaling methods as they have been done in MSWX and AgERA5, significantly improved the reanalysis dataset compared to the direct model output. Nevertheless, in the southwest of the Caspian Sea, the bias-corrected MSWX and AgERA5 show more errors than ERA5-Land. In general, the values of bias and RMSE in all three datasets are affected by the physical schemes of the model, parameterization, and data assimilation system, or the downscaling and bias correction methods. However, sources of bias can be different in different seasons of the year. The monthly spatial distribution of temperature in Iran shows that the minimum temperatures are located in the middle of Alborz and the northwest mountains of the country, and the coldest month of the year is January with a temperature of -10.8℃. The maximum temperatures in Iran are located in the southwest of the country and the southern coasts, and the hottest month of the year is July with an average temperature of 42.38℃.

This research was conducted with the aim of investigating the day and night temperatures in Iran. For this purpose, the minimum and maximum temperatures during 40 years (1981-2020) were examined using the AgERA5 dataset. Then, the diurnal temperature range (DTR) was calculated. In order to evaluate the performance of the AgERA5 dataset, the data from 56 meteorological stations and RMSE and R2 metrics were used, and the Theil-Sen test was used to analyze the average trend. The results of the evaluation of the minimum and maximum temperatures showed that the AgERA5 dataset has high accuracy for temperature estimation. The trend showed that the monthly trend of minimum and maximum temperatures in Iran is increasing. The increasing trend of temperature over time is not constant and its rate varies in different months. However, the increasing trend of temperature during different months of the year is consistent for the two variables of minimum temperature and maximum temperature. In all months, the maximum temperature increase is observed in winter and March. The DTR index in Iran is a minimum of 0.48 and a maximum of 16.6 °C, which occurs in December and July, respectively. The maximum DTR occurs in the interior dry regions and the minimum occurs in northern and northwestern Iran. The maximum increasing trend of minimum and maximum temperatures is in March, which increases by 0.8 oC/decade and 1.2 oC/decade, respectively. In contrast to the maximum temperature, there is a decreasing trend of the minimum and maximum minimum temperature in November, which decreases by -0.1 oC/decade and -0.2 oC/decade, respectively.

Dust emission is considered as one of the environmental hazards in arid and semi-arid regions. Understanding the effective variables in increasing dust mass density is very important for early warning and reducing its imposing damages. One of the main and effective variables in the occurrence of dust is the geographical and climate characteristics of the origin areas and areas affected by this phenomenon. Feeding the great rivers of Mesopotamia, it has reduced soil moisture. Also, the wind component is one of the reasons for the increase in dust in these areas. This study examines the relative importance of climatic variables to investigate seasonal and monthly changes in dust emission in West Asia and parts of South and Central Asia.

This study has examined West Asian dust from three perspectives spatial distribution, trends, and their relationship with climate variables. For this purpose, the Dust Column Mass Density (DUCMASS) variable output of the MERRA-2 dataset was used to investigate the spatial distribution of the dust mass density trend, and the AgERA5 dataset was used to investigate the seasonal and monthly changes of precipitation, wind speed, and temperature variables from 1981 to 2020. In this study, the modified Mann-Kendall (MMK) trend test method was used to investigate the trend of dust occurrence in the study area, and the Sen's slope estimator (SSE) test was used to investigate the slope of the trend and to better display the changes in dust mass density in the western region. the results of the SSE test have been examined on a decade scale.

Investigating the possible climate drivers in the changes of dust mass density for different regions by calculating the correlation between the time series of dust mass density and the variables of temperature, precipitation, and wind speed has been investigated. The results showed that there is an inverse correlation between dust mass density and precipitation and a direct relationship between dust mass density and temperature and wind speed. The highest correlations between dust mass density and temperature have been calculated, and this value has reached 0.9 in the warm months of the year. On the other hand, the highest negative correlations have been calculated in the cold period of the year (winter and autumn seasons) between dust concentration and precipitation with a value of -0.7. Thecorrelation coefficient between dust mass density and wind speed in the months of January to May and November to December was mostly above 0.6. This value shows a lower correlation in the summer season.In most months of the year, dust mass density shows an increasing trend in most regions, from March to July, an increasing trend in active dust springs in Mesopotamia, the deserts of Iraq and Syria, the desert of Rub' Al Khali, Ad-Dahna and Al Nufud Al Kabir were observed in Arabia and Thar desert in Pakistan. This increasing trend started cyclically from the beginning of spring and reaches its peak in June and July, and the intensity of the trend decreases from September and reaches its minimum value in December. The important point is that the cycle of changes in the monthly trend of dust mass density coincides with the cycle of changes in dust mass density. The northern parts of Iran and Turkey have the highest frequency among different months of the year with a decreasing trend of dust mass density. The increasing trend of dust mass density in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and the southeast of Iran was significant at the level of 0.05.

The results revealed that the seasonal changes in dust mass density show well the active sources of dust in the studied area. In the spring and summer seasons, the activity of the dust centers located in the west of the study area, including the Rub' al Khali, Ad-Dahna and Al Nufud Al Kabir deserts, Mesopotamia, the deserts of Iraq and Syria, increases and on the arrival of dust to the west and southwest Iran affects. The investigation showed that climate variables play a key role in the variability of dust mass density in the study area so the areas corresponding to the summer north wind and the 120-day wind of Sistan have shown the highest dust mass density in annual variability. The correlation coefficient between dust mass density with temperature and direct wind speed and its correlation with negative precipitation have been obtained. The results showed that dust mass density has an increasing trend in most of the regions, so from March to August (spring and summer), the increasing trend of dust mass density is significant at the level of 0.05. The highest intensity of the increasing trend was observed in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and southeast Iran.

The current research was conducted to evaluate the effectiveness of the reanalysis data of Era-interim, Agera5, and station data to simulate runoff using the Sacramento model in the Shapur watershed. Focusing on an innovative approach, this study provides solutions for integrating reanalysis data into runoff modeling that can improve the flexibility and accuracy of hydrological models. The research data were analyzed by statistically comparing the simulated discharge with the observed discharge at the watershed outlet on a daily time scale. The results showed that the discharge simulated by the station precipitation data with a correlation coefficient of 0.93 with the observed discharge performs better than the discharge simulated by the reanalysis data. Also, among the reanalysis data, the Agera5 data with a correlation coefficient of 0.82 performs better than the Era-interim data. The results of Era-interim data show an underestimation in the amount of precipitation, which is because it is located in the Shapur watershed (the coastline bordering the Oman Sea and the Persian Gulf). Also, in the review of the long-term monthly data, the reanalysis data in the hot months of Dubai has not been correctly simulated, which is due to the durability and low thickness of the clouds, and this has caused a decrease in the accuracy of the amount of precipitation. Finally, due to the proper distribution of rain gauge stations in this basin, the results obtained from the station data are better than the reanalysis data.