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Extreme precipitation is the leading cause of the flood, soil erosion, and drought with significant socioeconomic impacts on human resources. Therefore, projecting future precipitation changes, especially the intensity of extreme precipitation (IEP) in the future, is very important. This study’s main objective is IEP projection in Iran based on CMIP6 bias-correction (BC) multi-model ensemble (MME). The daily precipitation data of five CMIP6 BC models with 0.5 ͦ horizontal resolution was used under three shared socioeconomic pathways; SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Simple Daily Intensity (SDII) and maximum consecutive 1-day precipitation (RX1day) were used to measure IEP changes. Two Kling Gupta efficiency (KGE) and percent bias (PBIAS) methods evaluate the performance of the models, and the independence weighted mean (IWM) method was applied for ensemble averaging. Among the studied CMIP6 bias-correction models, the IPSL-CM6A-LR model has more underestimation than other models with a KGE score of 0.751 has the lowest performance and the MPI-ESM1-2-HR model (0.768) showed the highest performance. In general, the study results showed the uncertainties in CMIP6 models for precipitation, according to which no single model is reliable even in the BC method. The PBIAS in the region affected by Asian summer monsoon (ASM) in Iran is about 10%, based on which the bias of the mentioned models for monsoon precipitation is high. SDII and RX1day anomalies in all climatic zones of Iran except for the RX1day index in Cfa climatic zones in other zones and scenarios during the two periods 2021-2060 and 2061-2100 are positive. Also, the trend and slope of the IEP are increasing in all zones except for BWh, BWk, and Cfa zones for SSP1-2.6 and SSP3-7.0 scenarios. Investigation of trend IEP changes showed that the maximum of these changes will occur in the BWh climate zone, and the minimum would occur in the cold and mountainous climate zone of Iran (Dsc).

This study investigated the relationship between climate and biophysical variables in burned areas in Iran. The fire burned area (FBA) product (Fire CCI 5.1.1), land surface temperature (MOD11C3C), vegetation index (MOD13A1), and climate variables such as temperature, wind speed, relative humidity, and volumetric soil moisture from the ERA5 reanalysis dataset were used. Pearson correlation coefficient was used to determine the relationship between biophysical and climate variables and fire occurrence. The results show that FBA increased by 1.7 hectares/decade from 2001 to 2020. The high FBA in 2010 (the black summer of Iran) was due to high temperatures and significant heatwaves that led to extensive wildfires. Although anthropogenic activities are considered a significant cause of wildfires, several variables, including increased temperatures, less precipitation, relative humidity, and wind speed and direction, contribute to the extent and occurrence of wildfires. The country’s FBA hotspot is in the Arasbaran region during the summer season. Temperature and relative humidity are the most significant variables influencing the occurrence of wildfires. The results show the vulnerability of Iran’s forests and their high potential for fires. Considering the frequency of fire occurrences in Iran and the limited equipment, fire prevention plans should be carried out by applying proper management in high-risk regions.

The present study aimed at investigating temporal-spatial patterns and monthly patterns of temperature in Iran using new spatial statistical methods such as cluster and outlier analysis, and hotspot analysis. To do so, climatic parameters, monthly average temperature of 122 synoptic stations, were assessed. Statistical analysis showed that January with 120.75% had the most fluctuation among the studied months. Global Moran’s Index revealed that yearly changes of temperature in Iran followed a strong spatially clustered pattern. Findings showed that the biggest thermal cluster pattern in Iran, 0.975388, occurred in May. Cluster and outlier analyses showed that thermal homogeneity in Iran decreases in cold months, while it increases in warm months. This is due to the radiation angle and synoptic systems which strongly influence thermal order in Iran. The elevations, however, have the most notable part proved by Geographically weighted regression model. Iran’s thermal analysis through hotspot showed that hot thermal patterns (very hot, hot, and semi-hot) were dominant in the South, covering an area of 33.5% (about 552,145.3 km2). Regions such as mountain foot and low lands lack any significant spatial autocorrelation, 25.2% covering about 415,345.1 km2. The last is the cold thermal area (very cold, cold, and semi-cold) with about 25.2% covering about 552,145.3 km2 of the whole area of Iran.

The performance of bias-corrected precipitation of the Coupled Model Intercomparison Project Phase 6 (CMIP6) was evaluated using gauge observations from 45 synoptic stations. The spatial distribution and anomaly of two absolute thresholds of heavy (R10mm), very heavy (R20mm), and two relative thresholds of very wet days (R95p) and extremely wet days (R99p) of precipitation were investigated for the historical period (1975–2014) and two future periods (2021–2060 and 2061–2100) in Iran. Five CMIP6 models were evaluated, including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL by using root mean square error (RMSE), mean bias error (MBE), percent bias (PBIAS), correlation coefficient (PCC) statistics, and receiver operating characteristic (ROC) analysis. An ensemble of the models was generated by applying the independent weighted mean (IWM) method. Among the five models, maximum overestimation and minimum underestimation of precipitation amount are seen in IPSL-CM6A-LR and UKESM1-0-LL, respectively. The maximum R10mm and R20mm computed 35.10 and 18.50 days/year for the historical period, which were identified over the south of the Caspian Sea and Zagros Mountain chain. The anomaly of R10mm and R20mm in all river basins of Iran is positive, except for the four basins of Karkheh, Karun, Tashk-Bakhtegan, and Maharloo for the SSP5-8.5. Precipitation thresholds for both indices will be increasing over the coming decades on the coast of the Caspian Sea, while the index for R95p in the western and southwestern regions and R99p in the southern regions of Iran will experience a decreasing trend.

Iran, located in arid and semi-arid regions, has faced significant weather and climate extremes in recent years. This study aims to investigate the climate-related hazards associated with precipitation and temperature in Iran during the Hindcast period (1991–2019) and the Forecast period (2023–2028) using the Near-term Climate Prediction (NTCP) project. We investigate compounding climate-related hazards to assess the severity and importance of weather and climate extremes. To accomplish this, we integrated ten climate extreme indices, namely heavy precipitation (R10mm), the Simple Precipitation Intensity Index (SDII), heat wave frequency (HWF), heat wave duration (HWD), cold wave frequency (CWF), and cold wave duration (CWD), along with the Standardized Precipitation Evapotranspiration Index (SPEI-12), which further encapsulates drought frequency (DF), drought duration (DD), drought severity (DS), and drought intensity (DI). The CMIP6-DCPP models effectively simulate climate extremes and their seasonal cycle across Iran, with minor discrepancies in arid and mountainous regions due to data limitations. The result demonstrates a significant anticipated rise in drought frequency and heat wave events throughout the country within the near-term forecast period. Future projections indicate a shift in precipitation patterns, with increased heavy precipitation in the Zagros Mountains and southwest regions alongside more frequent but less intense droughts nationwide. Heat wave frequency and duration are projected to increase, particularly in southern Zagros and eastern and western Iran, with high-altitude areas experiencing accelerated warming. The results project a shift in climate risk distribution over the next decade, with low to moderate-risk areas decreasing by approximately 15.7% and high-risk areas increasing by roughly 10%, encompassing over 36% of Iran’s total area by 2028. Integrated risk maps reveal high to very high compound climate hazard levels across large parts of Iran, necessitating urgent adaptation planning, especially in western, southern Zagros, and eastern regions. Sensitivity analysis confirms that identified multi-hazard hotspots in Iran are spatially robust and statistically significant, reflecting the dominant influence of key climatic extremes.

The extreme temperature indices (ETI) are an essential indicator of climate change. The detection of their changes over the next years can play an essential role in the climate action plan (CAP). In this study, four temperature indices (mean of daily minimum temperature (TN), mean of daily maximum temperature (TX), cold-spell duration index (CSDI), and warm-spell duration index (WSDI)) were defined by ETCCDI and two new indices,the maximum number of consecutive frost days (CFD) and the maximum number of consecutive summer days (CSU), were used to examine ETIs in Iran under climate change conditions. We used minimum and maximum daily temperatures of five general circulation models (GCMs), including HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, and NorESM1-M, from the set of CMIP5 bias-correction models. We investigated two representative concentration pathway (RCP) scenarios of RCP4.5 and RCP8.5 during the historical (1965–2005) and future (2021–2060 and 2061–2100) periods. The performance of each model evaluated using the Taylor diagram on a seasonal scale. Among models, GFDL-ESM2M and HadGEM2-ES showed the highest, and NorESM1-M and IPSL-CM5A-LR showed the lowest performance in Iran. Then, an ensemble model was generated using independence weighted mean (IWM) method. The results of multi-model ensembles (MME) showed a higher performance compared to individual CMIP5 models in all seasons. Also, the uncertainty value significantly reduced, and the correlation value of the MME model reached 0.95 in all seasons. Additionally, it is found that WSDI and CSU indices showed positive anomalies in future periods, and CSDI and CFD showed negative anomalies throughout Iran. Also, at the end of the twenty-first century, no cold spells are projected in almost every part of Iran. The CSU index showed that summer days are increasing sharply; according to the results of the RCP8.5 scenario in spring (MAM) and autumn (SON), the CSU will increase by 18.79 and 20.51 days, respectively, at the end of the twenty-first century. It projected that in the future, the spring and autumn seasons will be shorter and summers will be much longer than before.

Iran is a semi-arid and arid country in Western Asia and is exposed to numerous local and trans-regional dust systems due to its location in the global dust belt. The present study sought to investigate the change-point detection (CPD) and trend of the number of dusty hours (NDH) in Iran over a long-term period (1980–2015). The station dust frequency (SDF) of 81 synoptic stations was first hourly obtained and then processed. Furthermore, the dust aerosol optical depth (DOD) was obtained hourly from the Monitoring Atmospheric Composition and Climate (MACC). The results indicated the maximum dust frequency with 21.28 days at 12 GMT due to surface heating and the occurrence of local dry instabilities. The minimum dust also occurred with 7.76 days at 00 GMT in Iran. SDF and DOD had a direct relationship, but they had inverse significant relationships with altitude and latitude in Iran. The maximum average trend of the whole of Iran at 21 GMT with a value of Z 1.83 was significant at a 90% level, indicating an increase in nocturnal dust in Iran. The southwest of Iran, especially Bostan, Omidiyeh, and Masjed-Soleyman stations, had maximum numbers of dusty days so that NDHs of Omidiyeh station were increasing at 18 GMT (2.84 years−1 days). The year 2000 was, on area-averaged, the dust CPD in the across Iran, but 2007 and 2008 were the most frequent CPD of NDHs. None of the hours had lower amounts of dust after the CPD than before the CPD, indicating a significant increase in the dust of Iran.

Climate and especially air temperature have a direct and high impact on nut trees in arid regions. In this regard, the use of multi-model ensemble projections as more reliable models is important. In this study, based on multi-model ensembles (MME) from the coordinated regional climate downscaling experiment Middle East North Africa (CORDEX-MENA), the air temperature in RCP8.5 and RCP4.5 pathways in pistachio cultivation areas in Iran was projected. The period 1980 to 2017 was identified as the reference period. Due to the sensitivity of climate change and the existing uncertainties, the hourly temperatures based on the models of chilling hours (CH) and chilling portions (CP) were also used for the reference period. The results of CORDEX simulations for pistachio cultivation areas suggest that the minimum and maximum annual air temperature (MMAT) and growing season temperature (GST) at the end of the current century compared to 1980–2017 as the reference period will increase significantly to 4 °C. The downward trend of winter chill accumulation during the cold season based on observational data will confirm the increase in air temperature in pistachio cultivation areas in Iran. In the output simulation of CORDEX project, the date of flowering pistachio tree for late-maturing varieties by 2080 under representative concentration pathway (RCPs) RCP8.5 and RCP4.5 will occur 16 days and 6 days sooner, respectively. The amount of heat accumulation or growing degree days (GDD) in the future in pistachio cultivation areas will be increased, and deviation from the historical period, based on RCP8.5 and RCP4.5 on average, will occur 3200 and 2600 GDD, respectively.

Efficient and proper understanding of the state of the clouds regarding different seasons of the year will have profound effects on different economic and environmental sectors. The purpose of this study is to determine the hourly dissociation of ice and liquid clouds in Iran. To this end, cloud optical thickness (COT) data, as well as optical depth of clouds in two phases of liquid and ice were obtained and processed from 31 synoptic meteorological stations (1960–2015), MODIS data from Terra satellite during the years 2001 to 2011, and they were processed then. Next, using the RegCM4 model, the cloud fraction (clt) was simulated to accurately identify the cloud cover situation in Iran. The results showed that the maximum annual mean abundance of liquid and ice clouds was 18.95 days for the time 15:00 and 3.99 days for the time 06:00, respectively. Climatic zones of the Caspian and Persian Gulf coasts at 15 o’clock had the highest decreasing trend of liquid clouds. Ice clouds in all parts of Iran’s climate, with the exception of the eastern plateau, also declined. From south to north and east to west of Iran, the occurrence of ice and liquid clouds is increasing. Therefore, the spatio-temporal distribution of liquid and ice clouds in the country was also dependent on spatial components and latitude had the greatest impact. From the satellite and modeled data, the RegCM4 model has been able to detect the Monsoon phenomenon in southeastern Iran during the summer. CLT simulation in Iran has also shown that cloud cover in Iran fluctuates between 28 and 65% on average, with 81.5% of Iranian stations having a significant change in the amount of annual cloud cover. Correlation of liquid and ice clouds with precipitation showed that liquid clouds in summer and ice clouds in spring had higher correlation with precipitation in Iran. Northern coasts of Iran due to greater ascent mechanisms such as coastal compressors, north latitude atmospheric circulation systems, and maximum winds in the north and west of Iran due to the location of western systems entry and sufficient thermal gradient, had maximum ice clouds in the last half century. Also, south of Iran, despite having extended and great water-bodies, is less cloudy due to descending air in Hadley’s circulation (Hadley cell) of air.

The present study aimed at studying the spatial–temporal behavior of precipitation received over a long period of time in the northern strip in Iran. To this end, the available data related to the precipitation in the 50 years (1957–2007) were extracted from APHRODITE with a spatial resolution of 0.25° × 0.25°. Some statistical analyses such as dispersion statistics and the analysis of trend were run on the extracted data to obtain the precipitation changes in a decade, using MATLAB software. The results of the analyses showed that the precipitation changes in decade in the northern strip in Iran followed a high clustering pattern due to the physiography of the region, convection and the influence of weather systems, and over time, precipitation is concentrated in this region of the north of Iran. Statistical analyses showed that precipitation pattern had a severe disorder. The simulation of the precipitation density function also showed a decrease in precipitation variance and a sharpening of the Gaussian function, indicating the absence of constant precipitation conditions in the northern strip of the country. Moreover, the assessment of trends through non-parametric method showed that the annual precipitation has a decreasing trend in the region. Analysis of the daily precipitation concentration demonstrated a widespread distribution, and, confirmed the irregularity and inequality of daily precipitation across the northern strip of Iran.