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.

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.

Global warming gives rise to higher probability of extreme temperature values and related events. Social and environmental impacts of these events are great at local scale. Most of the research that has been done on this issue in different countries denotes decrease of cold extremes and increase of warm extremes. To examine such changes, we used daily temperature data in 1951-2003 Period from 27 Iranian synoptic stations that have homogenous and high quality data and covering standard normal period (base period) 1961-1990. We used extreme temperature indices in five categories of percentile-based, absolute, threshold, duration, and other ones.Some of our obtained results show negative trends for extreme indices like number of frost days (FD), number of ice days (ID), cool days (TX10p), and cool nights (TN10p) in most stations under study. For instance, decrease of 8 to 10 frost days per decade in the northeast of the country is noteworthy. Conversely, we got positive trends for indices such as summer days (SU25), warm days (TX90p), and warm nights (TN90p) in most stations under study. Increases of 5 to 7 warm days per decade in Shiraz and Tehran are remarkable. We found all three behaviors of stationarity, positive and negative trends for indices such as warm and cold spell duration indices (WSDI and CSDI). Compared to other indices, spatial and temporal changes of the above two indices could not be interpreted well. Apart from some exceptions, our results were in agreement with the latest findings of Intergovernmental Panel on Climate Change (IPCC) and other worldwide studies.

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.

In recent years, the importance of climate prediction has increased as a scientific source for understanding climate change and evaluating its consequences in political and economic decisions. Providing predictions with less uncertainty, especially for precipitation and temperature is of considerable importance for policymakers in time periods from several months to several decades. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability and variability. The DCPP consists of three components (A, B, and C). Component A comprises of the production and analysis of an extensive archive of retrospective forecasts. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts, and Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (Boer et al. 2016). The aim of this study is to predict precipitation extremes using the decadal Climate Prediction Project contribution to the Coupled Model Intercomparison Project Phase 6 (CMIP6) for the period 2021 to 2028 over Iran. For this purpose, two types of data including 77 synoptic stations and three DCPP models (BCC-CSM2-MR, MPI-ESM1-2-HR, and MRI-ESM2-0) with a horizontal resolution of 100 km were used. The precipitation output of DCPP models, each with nine variants (27 members) were used for two time periods, including Hindcast (1981-2019) and Forecast (2021-2028). To evaluate DCPP models, we used the Root Mean squared error (RMSE), the Pearson correlation coefficient (PCC), the Mean Bias Error (MBE), the Percent bias (PBIAS), and the Taylor diagram methods. In addition, Direct Model Output (DMO) was corrected by the Delta Change Factor (DCF) method, and the Independent Weighted Mean (IWM) was used to generate a multi-model ensemble from 27 members. In this study, the ETCCDI indices including days with Heavy precipitation (R10mm), days with Very heavy precipitation days (R20mm), Simple daily intensity (SDII), The maximum 1-day precipitation amounts (Rx1day), The maximum 3-day precipitation amounts (Rx3day), The maximum 5-day precipitation amounts (Rx5day) were calculated to analyze precipitation extremes for all regions of Iran. Furthermore, the evaluation of the DCPP models showed that the output of mentioned models is acceptable for all regions of Iran. Also, the performance of CMIP6-DCPP-MME is higher than the individual models. The result of the prediction of precipitation extremes showed that the six studied extreme precipitation indices will increase for the next decade. The Southwest and Northeast are the two hotspots of positive anomaly. In contrast, the southern coast of the Caspian Sea for the R10mm index will experience a negative anomaly for the next decade. The findings show that the southeastern region of Iran, from the eastern borders to the north of the Strait of Hormuz, will be the main area of negative precipition anomalies in the country in the next decade. So that the indices of days with heavy (R10mm) and very heavy (R20mm) precipition will decrease by 2.7 and 0.3 days, and daily precipition intensity (SDII) will decrease by 2.6 mm/day.

Global warming in recent decades has caused significant changes in precipitation and temperature, including changes in the mean and standard deviation of these variables and changes in the intensity and frequency of climatic extremes (floods and droughts). Given the importance of these changes in water resources management, it is crucial to study the trends in these variables. In this study, in 12 selected stations in different climatic regions in Iran, the changes in monthly and annual precipitation and mean temperature during 1961-1990 and 1991-2020 were examined. The results of Mann-Kendall test showed in most stations precipitation had an increasing trend in the first period, and a decreasing trend in the second period; although in both periods the trend was not significant (Z<1.645). The mean temperature has increased in both periods, which in the second period has increased with a higher level of confidence (Z>2,576) and greater slope than in the first period. The average annual rainfall has decreased in most stations, and the average annual temperature has increased in all stations. The distribution of precipitation and temperature showed that in some stations, the probability of occurrence of extreme events and hot and cold periods in the second period has increased compared to the first period. In some other stations, droughts/floods are more/less likely to occur. This indicates that the activity of air masses affecting each station can be intensified or weakened due to climate change.

Two single and multi-site statistical downscaling methods of Statistical Downscaling Model–Decision Centric (SDSM–DC) for daily temperature and precipitation are evaluated at nine stations located in the mountainous region of Iran’s Midwest. SDSM is best described as a single-site model, but it can be extended to multi-site applications via conditional resampling (CR-SDSM, Wilby et al.2003; Harpham and Wilby 2005). SDSM–DC (Wilby and Dawson 2013) is a hybrid of the stochastic weather generator and transfer function methods. Predictor selection is based on empirical relationships between GCM-scale predictors and single-site predictand variables. (Farajzadeh et al.2015). Applying SDSM to multi-site daily rainfall downscaling includes two steps: (1) the daily rainfall and temperature at a “marker” site (in this study, the area average amounts) is first downscaled by the single-site SDSM; (2) Daily rainfall amounts are then “resampled from the empirical distribution of area averages, conditional on the large-scale atmospheric forcing and the stochastic error term. The actual daily rainfall is determined by mapping the modeled normal cumulative distribution value onto the observed cumulative distribution of amounts at the marker site” (Wilby et al.2003; Liu et al.2013). Ultimately, the marker site rainfall is resampled to the constituent amount falling on the same day from each station in the multi-sites array (Harpham and Wilby 2005). Thus, if the marker series is based on an unweighted average of all sites, the conditional resampling will preserve both the areal average of the marker series and the spatial covariance of the multi-site rainfall (Wilby et al.2003). Additionally, using area average, instead of individual sites as the marker series, reduces the risk of employing a nonhomogeneous/non-representative record and increases the signal to noise ratio of the predictand (Wilby et al.2003; Liu et al.2013). To downscale temperature, the same steps are applied but unconditionally using transfer function methods. For statistical downscaling, two sets of data are generally required: (1) observational data for model calibration and validation, as predictands; and (2) synoptic-scale climate data from GCM and/or reanalysis, as predictors. In order for a better assessment of climate variability and change on local and regional scale, long-term time series of reliable climate data at fine-scale resolution are required (Vincent et al 2002; Mekis and Vincent 2011; Menne et al 2012). As mentioned before, for the Midwest of Iran, we selected nine synoptic stations with nearly complete data coverage for 1981–2010. We used station data for two decades (1981–2000) for calibration and from one decade (2001–2010) for validation of daily values of minimum and maximum temperature, and total daily precipitation. To assess the accuracy and homogeneity of the observational data, we used different methods for quality control: the R packages RHtestsV3 (Wang and Feng 2010) and RHtests_dlyPrcp (Wang et al.2010), based on penalized maximal t and F tests (Wang et al.2007; Wang 2008b) that are embedded in a recursive testing algorithm (Wang 2008a); the R package Climatol (Guijarro 2012), which applies a type II linear regression model; and SDSM (Wilby and Dawson 2012, 2013) based on reanalysis predictor variables. Missing values are filled in by using the sequential k-nearest neighbor imputation method (Kim and Yi 2008) and homogeneity tests are applied both before and after infilling to assess infilling performance. Predictor fields are extracted from the National Centers for Environmental Prediction (NCEP) Reanalysis (Kalnay et al.1996) archives at resolutions of 2.5°×2.5°. As mentioned earlier, SDSM has its own methodology for predictor selection in which EOFs of NCEP reanalysis data over the domain (30° N, 42° E) and (40° N, 52° E) are screened separately for temperature and for precipitation. (Farajzadeh et al.2015). Results indicate that the methods are of widely varying complexity, with input requirements that range from single point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981-2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. Single (multi) -site method of SDSM, passing 76% (81%), 16% (7%) and 14% (5%) of the Kolmogorov–Smirnov (KS), the Pearson correlation and the combined tests, performed well in terms of temperature and precipitation downscaling. Single-site method performed better than multi-site one at single sites; however, multisite method performance is better at regional downscaling. Correlation tests were passed less frequently than KS tests. Both methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.