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.

IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake basin is one of the most vulnerable areas to climate change. Urmia lake basin has received the special attention of policymakers and planners since it is the location of Lake Urmia, and it also holds nearly 7% of Iran's water resources. A huge program of dam construction and irrigation networks has been started in this basin in the northwest of Iran since the late 1960s. Despite the increasing attention to Lake Urmia since 1995, the water level of this lake has decreased. During the drought of 1990-2001, Lake Urmia experienced a decrease in its level without any recovery and is decreasing at an alarming rate. Therefore, it is necessary to project the future climate of the Urmia Lake basin and especially extreme precipitation based on the latest climate change models. Materials and MethodsThe CMIP6 models were used to investigate the future projection of extreme precipitation in the Lake Urmia basin. Considering the horizontal resolution, availability of daily data, and climate sensitivity, we selected five models including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The horizontal resolution of all five models is 0.5o. The 25-year historical period (1990-2014) and the 25-year projection period for the near future (2026-2050) were chosen to analyze the extreme precipitation in the Urmia Lake Basin. The future projection was considered under three shared socioeconomic pathways (SSPs) scenarios. These scenarios include SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Mean bias error (MBE) and Normalized Root Mean Square Error (NRMSE) were computed to evaluate the individual models and the multi-model ensemble generated by Bayesian Model Average (BMA) method. To assess extreme precipitation, we used four indices including the Number of heavy precipitation days (R10mm), the number of very heavy precipitation days (R20mm), the Maximum 1-day total precipitation (Rx1day), and the Simple Daily Intensity Index (SDII). Results and DiscussionThe performance of five CMIP6 individual models and the multi-model ensemble in the Lake Urmia basin during the period of 1990 to 2014 was evaluated against eight ground stations. The investigation of the annual precipitation showed that this variable is underestimated in CMIP6 models in the basin averaged. The maximum and minimum bias values model was seen in Saqez station by -9.64 mm for the MRI-ESM2-0 and -0.43 mm for the UKESM1-0-LL, respectively. The highest average MBE in the Urmia Lake basin was respectively obtained for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL models. Among the examined models, MPI-ESM1-2-HR has shown the highest efficiency among the examined individual models.Variations in the number of heavy precipitation days during the historical period (1990-2014) have distinguished three main areas for the Lake Urmia basin. The main hotspot of heavy precipitations in the Urmia Lake basin is located in the southwest of Kurdistan province with a long-term average of 25.4 days. The next hotspots are the northwest and the northeast of the basin. In the historical period (1990-2014), the precipitation intensity index Rx1day experienced considerable variability. Based on CMIP6-MME, the value of the Rx1day index in the Urmia Lake basin is estimated between a minimum of 16.3 mm and a maximum of 63.3 mm. The maximum variation of this index is seen in the southern areas of the basin, especially on the border with Iraq. ConclusionEvaluation of individual CMIP6 models showed that these models underestimated precipitation in the Lake Urmia basin during the historical period (1990-2014). The CMIP6-MME has significantly improved precipitation estimation. The results of the investigation of days with heavy and very heavy precipitation showed that the two indices R10mm and R20mm are increasing in most areas of the Lake Urmia basin by the middle of the 21st century. Trend analysis showed that the days with heavy and very heavy precipitation will increase under different SSP scenarios in most areas of the Lake Urmia basin, especially in the northern and western regions. Also, days with heavy and very heavy precipitation will have a greater contribution than normal precipitation days in the future. It is expected that the intensity of precipitation will increase in the coming decades in the Lake Urmia basin, and this increase is more for the western and northern regions than for other regions of the basin. This result may potentially increase the flood risk in Lake Urmia.

Evaluating the susceptibility of regional climates to climate change provides a framework for realistically analyzing potential future climate changes. This paper investigates the impact of human activities on variations in extreme precipitation in Iran by evaluating data provided from 286 rain-gauge stations during 1967-2010 and general circulation simulation results of the CanESM2 model. This investigation was based on six forcing factors, including natural external factors (volcanic aerosols, solar radiation), anthropogenic and a combination of them, Green House Gases (GHGs), changes in land use, and anthropogenic aerosols. Seven precipitation indices, namely Rx1day (annual maximum 1-day precipitation), Rx5day (annual maximum 5-day precipitation), R10mm (annual count of days with daily precipitation exceeding 10 mm), R20mm (annual count of days with daily precipitation exceeding 20 mm), CDD (consecutive dry days), CWD (consecutive wet days), and PRCPTOT (annual total wet day precipitation), have been analyzed via the optimal fingerprint method. The results revealed that Rx1day, Rx5day and CWD increased, while R10mm, R20mm, CDD, and PRCPTOT decreased among which CDD and Rx1day indices showed significant variations, with values of 18.4% and 10.9%, respectively. Furthermore, the obtained results implied that only the impact of anthropogenic forcing, with a value of 1.4, was detected and attributed to variations in CDD. Additionally, anthropogenic forcing caused an increase in the return period of a 20-year event by 1.9 years for CDD. Although human-induced forcing factors presented marked trends in some cases, their effects were not generally detected and attributed to the change in the observations, apart from one exception.

In disaster risk management, recognizing, predicting and anticipating its occurrence plays an important role in the losses reduction. Therefore, identifying extreme events and examining changes in their occurrence is very important. For this purpose, according to the guidelines of the World Meteorological Organization (WMO) the extreme events that occurred in East Azerbaijan during in the last 10 and 5 years (1388-1397) were analyzed. The studied indicators are: heat waves, cold waves, extreme precipitation, dust and hail. During the study period, different regions of the province have been affected by climate extreme events. Heat indices show that during the last 5 years, the heat wave, its length and intensity has increased compared to the 10-year period, which indicates the impact of climate change in recent years. The cold wave and its intensity have increased in the last 5 years, but the cold wave length has decreased relatively. Heavy rainfall has increased in the northern half of the province over the past 5 years. The number of hail events does not show a noticeable trend during 10 years and its distribution is almost the same for 10 and 5 years`. The cities of Maragheh, Tabriz, Ahar, Jolfa, Varzeqan, Sarab, AjabShir and Shabestar have experienced the most extreme events during the last 10 years, respectively.

This study was aimed to investigate Precipitation extremes variability in Bakhtegan Basin. Climate data corresponding to Bakhtegan basin was extracted from AgMERRA dataset for the study time period (1980-2010) using R software. Daily precipitation data was also extracted from the meteorological stations data arhive in the basin during the study period. Rx1day, Rx5day, PRCPTOT, CDD, R10mm, R20mm, R95p and R99p indices were selected and calculated to study climate change in the region. RX1day results with the changes trend of -1.186 to 0.217, RX5day, -0.624 to 0.82, R10, -0.179 to 0.025, R20, -0.06 to 0.046 and PRCPTOT, -3.675 to 2.028 showed a decreasing trend in catchment’s most parts and jointly in the catchment’s southern and western parts. Decreasing precipitation, the consecutive dry days (CDD) increased and generally showed an increase throughout the basin (except for a small section in the catchment’s centeral and eastern parts). The most consecutive dry days were observed 259 days in 2008, 245 in 1983 and 264 in 1999 in Shiraz, Doroudzan Dam and Aliabad Kamin stations, respectively. Both R95p and R99p indices illustrated increasing and decreasing trends in different parts of the basin.