Projecting the impacts of future climate change on precipitation in the Kashafrud catchment in Iran

Projecting the impacts of future climate change on precipitation in the Kashafrud catchment in Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Projecting the impacts of future climate change on precipitation in the Kashafrud catchment in Iran Hossain Imanipour, Mokhtar Karami, Abdolreza Kashki, Morteza Esmailnejad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5953463/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is known as one of the most critical challenges facing humanity. These changes seriously threaten the Kashafrud River, a significant water resource in northeastern Iran. This study aims to project the impact of climate change on precipitation in the Kashfrud catchment in Iran. The output data of global climate models, including ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 from the sixth-generation models (CMIP6), have been used to project future climate changes. Based on the accuracy of statistical criteria, the linear scaling (LS) method was employed to do the bias correction of precipitation data. Then, using the CMhyd model, the precipitation data were simulated for the two future periods (2025-54) and (2055-84) under the SSP1_2.6 and SSP5_8.5 scenarios. The average KGE and RMSE coefficients for the precipitation of the selected MIROC6 model were obtained as 0.91 and 21.09, respectively. Average annual rainfall under the scenarios of SSP1_2.6 and SSP5_8.5 in the near future period decreased by 2.92% and increased by 3.44%, respectively. Average annual rainfall was projected as follows: under the SSP1_2.6 and SSP5_8.5 scenarios in the near future period, it decreased by 2.96% and increased by%3.44, and under the SSP1_2.6 و SSP5_8.5 scenarios in the middle future period, it increased by 2.96% and 8.79%, respectively compared to the observation period (1991–2020). In terms of seasonal comparison, in most of the scenarios of the future periods, in the spring and autumn seasons, precipitation will decrease between 1.51% and 8.18%. Precipitation in the future seasons in summer and winter was projected with a slight increase. The results showed that climate change will have a significant impact on the precipitation of the study area. This can have severe consequences for different sectors, including agriculture, industry, and the environment. The research results can be employed as a management tool in the direction of water resources management. Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Hydrology Climate change CMIP6 Rainfall Kashafrud catchment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The excessive use of fossil fuels, land use change, the increase in the world’s population, and consequently, the increasing expansion of industrial activities to provide the welfare and needs of the population of the Earth have caused visible changes in the climate of the Planet gradually after the industrial revolution. The most obvious of these changes is the increase in the average temperature of the Earth and the increase in extreme climatic phenomena such as floods, storms, hail, tropical storms, heat waves, rising sea levels, melting of polar ice, and drought. The increase of these events in recent years has become the primary concern of climatologists and heads of countries of the world. The attention of public opinion and scientific societies to this issue and the concentration of greenhouse gases in the past few years has led to a kind of global cooperation in the investigation of this global problem because research has revealed that the temperature anomalies in many different parts of the globe Environmental issues such as floods, storms, and droughts are followed. One of the most important of these changes is the change in precipitation. Determining the seasonal or monthly fluctuations of precipitation can inform managers and planners in different departments and provide them with an accurate picture of future climate changes and fluctuations so that decisions can be made according to the upcoming weather conditions. Precipitation is a fundamental feature of the Earth’s hydrological cycle and can have many impacts on various human activities (Sun et al., 2006 ). Due to the change in the precipitation pattern, the intervals of precipitation have also changed and caused floods in some areas. The impact of climate change on the aggravation of drought is considered a more dominant phenomenon in different regions of the world (Wang et al. 2011 ). Climate change is expected to change the magnitude and spatial and temporal patterns of hydro-climatic variables, such as precipitation (Guo et al., 2020 ; Papalexiou & Montanari, 2019 ). Accordingly, evaluating the performance of existing models and specifying uncertainties and underlying biases in climate model simulations are essential to understand their value and potential for doing studies on climate change impact assessment (Raghavan et al., 2018 ; Rivera & Arnould, 2020 ; Zazulie et al., 2018 ). Recent estimates of global warming indicate the accuracy of the Coupled Model Intercomparison Project (CMIP) data, which has led to an increase in confidence in the accuracy of the project (Baker & Huang, 2014 ). Despite conducting numerous studies in the field of climate change and its impacts on different economic sectors by applying the outputs of various climate change models, using CMIP6 series models (Pbt a,n) and Shared Socioeconomic Pathways (SSP) scenarios of the Intergovernmental Panel on Climate Change (IPCC) around the world, so far, only a handful of studies with selected models. The models used in this research include ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 models in the Kashafrud catchment. Considering the vulnerability of different regions to climate change, it is imperative and necessary to study climate change based on the data and report of the sixth phase to investigate the impacts of climate change to adopt codified policies and plans and policies in various sectors. The purpose of this research is to project the impact of future climate change on the precipitation of the Kashafrud catchment. The results of this research can be used in the long-term planning of the agricultural and water resources management sectors and industry. Also, presenting these results can be very helpful in compiling upstream documents. The results of the climate models under the CMIP6 project scenarios promise to improve and strengthen the climate change project information for countries. In this regard, much research using different CMIP6 scenarios is being conducted in the world. 2. Review of literature In the past years, scientists and experts in geography and climatology have conducted extensive research on the impacts of climate change on precipitation and have tried to identify and explain the relationship between climate elements and factors by presenting different methods. Elguindi and Giorgi ( 2006 ) investigated the response of the sea level of Mazandaran to climate change for the years 1948 to 1990 using the Climate Model data for hydrologic modeling (CMhyd). They projected the changes in sea level height with the help of a simple hydrological equation. In their research, it was proved that this hydrological model has correctly projected the real changes in the sea level. Eyring et al. ( 2016 ) reviewed the design of the experiments and the structure of the CMIP6 models. Through the design and distribution of simulations of global climate models, the CMIP project has become one of the fundamental pillars of climatology. Kim et al ( 2020 ) compared the SSP and Representative Concentration Pathways (RCP) Scenarios in a study in the Han River catchment of South Korea. Their results have shown that the results obtained from the SSP scenarios with social and economic conditions attached to them are very different from the previous scenarios, including RCP. Therefore, this issue shows the importance of socioeconomic factors in climate change. Zamani et al. ( 2020 ) attempted to evaluate the performance of the CMIP5 and CMIP6 models in projecting the average precipitation on an annual and seasonal time scale in the north and northwest of Iran in the 1987–2005 period using statistics such as relative coefficient, correlation coefficient, Root Mean Square Error (RMSE), and relative error did. Their study is considered the first attempt to compare data from these two projection models in this region. The results showed that the simulated precipitation of the two projection models was different. Also, the relative bias for winter in all stations in CMIP6 models was lower than CMIP5, which indicates the better performance of the mentioned models, and in general, CMIP6 models had better performance than CMIP5. In a study titled “Precipitation and temperature zoning of Razavi Khorasan province using data from the sixth climate change report (CMIP6)”, Alamdari et al. (2024) examined the impacts of climate change on precipitation and temperature of 7 synoptic stations of Razavi Khorasan province, including the Mashhad station. Their results showed that in all scenarios, annual precipitation will experience an increase of 0.4 to 6.8%. Given the increased accuracy of CMIP6 models, it is necessary to visualize the future climate conditions of different locations based on the results of these models and to use the most complete of these new models. For this purpose, in the present study, the results of the latest available CMIP6 report and Earth System Model (ESM), which consider more components of the Earth system in climate modelling and have better capabilities compared to General Circulation Models (GCM) and also support leap years, were used. Given that several studies indicate the remarkable performance of various downscaling methods and models, including the CMhyd model, in projecting climate change patterns at the local scale, therefore, in this study, the CMhyd model was considered for simulating future climate data. In the following sections, the research method, study area and data, the performance of selected CMIP6 models, and the results and discussion are discussed. 3. Data and methodology 3.1. Study area The study area is the upstream part of the Kashafrud catchment as part of the Qara-Qom basin. The Kashafrud catchment is located in the northeast of Iran, between the longitudes of 58º and 2’ to 59 º and 45’ east and the latitudes of 36 º and 3’ to 37 º and 20’ north (Fig. 1 ). The total upstream of ​​the Kashafrud catchment is about 8000 km 2 , of which 2000 km 2 are plains, and the rest are highlands. The minimum and maximum elevations of the Catchment are 852 and 3309 meters, respectively. The existence of different elevations and the proximity of the Kashafrud catchment to the central desert play a significant role in the climatic changes of this region and have caused climate diversity in it. 3.2. Observational climate data It is necessary to compare its historical data with the station’s observational datasets using standard methods, which we will describe in detail below to compare and determine the performance of the models in the projecting process. Therefore, for the study area, the observational daily precipitation data of 12 weather stations from 1991 to 2020 were obtained from Razavi Khorasan Regional Water Corporation and Iran Meteorological Office, Iran. Table 1 shows the characteristics of the selected stations. Table 1 ) Characteristics of the selected upstream stations of the Kashafrud catchment No. Meteorological station Type station Longitude (D.D.) Latitude (D.D.) Elevation (meter) 1 Androkh Multi-parameter rain-gauge stations 59.66 36.58 1207 2 Ardak 59.29 36.73 1320 3 Dowlatabad 59.16 36.44 1575 4 Golestan 59.55 36.17 1240 5 Jaghargh 59.32 36.31 1434 6 Kardeh 59.67 36.65 1265 7 Olang-e Asadi 59.81 36.25 914 8 Radkan 59.01 36.81 1214 9 Sarasiab 59.33 36.39 1296 10 Tough 59.28 36.48 1176 11 Mashhad Synoptic 59.63 36.24 999.2 12 Golmakan 59.28 36.48 1176 The mean precipitation values of the 12 weather stations in the near future (2025-54) and middle future (2055-84) periods were simulated via CMhyd software under the selected scenarios. For this purpose, the output of climate models was extracted for all stations, and bias corrected. Then, the algorithm was applied to the historical and scenario data, and their data was modified, and the overestimation or underestimation of these data was corrected compared to the observed values. It is also necessary to apply statistical coefficients to estimate the accuracy of the model before using the results of each model. One of the necessities in the studies related to climate change is to check the quality control and homogeneity of the data pertaining to the stations in the study area. In this respect, cases such as the lack of statistics for precipitation data, the completeness and accuracy of the days of the months, and the check of outlier data were investigated. After performing the quality control of observational datasets, data homogeneity tests and homogenization of heterogeneous time series of precipitation were performed. Finally, the Linear scaling (LS) method was used to do the bias correction of the precipitation data, and the accuracy of the simulation of the precipitation variable in different stations of the study area was calculated using statistical criteria. Also, previous studies conducted in some stations of the study area (Rashidi Ghane et al., 2023) have introduced the LS method to do the bias correction of the precipitation variable in this area, which was also used in this study. 3.3. Climate change models and scenarios One of the main factors in the downscaling of micro-scale climate data is the screening of climate change models. 3.3.1. Historical climate data Historical climate data cover the period in which the observational datasets are available. Climate data verification is performed by comparing the historical climate data with observational data. At present, the period covered by the model used in this research is from 1991 to 2014. The use of SSP scenarios, which are a combination of RCP scenarios and socioeconomic policies, is one of the crucial differences between CMIP6 models compared to previous models. To run the CMhyd model, daily precipitation data is required. Therefore, the output of the selected models under the SSP1-2.6 and SSP5-8.5 scenarios for the Kashafrud catchment was obtained, after examining the error rate using different statistics, and exponential downscaling was performed using the CMhyd model. One of the major limitations in using the output of GCM models is their low spatial resolution, which does not correspond to the accuracy required by hydrological models in terms of space and time. Therefore, downscaling methods are used to overcome this limitation (Kamal & Massah Bavani, 2012). Considering similar research in the study area, ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 models, which were introduced and used in other studies (Babaeian et al., 2023 ; Nikakhtar et al., 2024 ; Zarrin & Dadashi-Roudbari, 2021) as models with better performance in this area, were used. The difference between this research from other research conducted in the study area is the use of a more significant number of stations, a different downscaling model, and the use of selected and proposed climate models. Next, precipitation values ​​were selected and applied using three CMIP6 ESM models, which, in addition to their initial screening in terms of climate model type (GCM or ESM), were selected and applied based on the availability of daily data, historical and future data, and the presence of temperature and precipitation data in two SSP1-2.6 and SSP5-8.5 scenarios, which represent low and high emission scenarios, respectively, in the study area. The projection by these models is a combination of a new set of emission and land use scenarios obtained by LAMs models based on the Shared Socioeconomic Pathways (SSPs) in the future period (including age, economic growth, urbanization, education, and population) and relation to greenhouse gas concentration scenarios (Eyring et al., 2016 ). Table 2 illustrates the characteristics of CMIP6 models. Table 2 Characteristics of CMIP6 models used in this research No Model name Horizontal resolution (Km) Institution/Country Reference 1 ACCESS-ESM1-5 192 * 144 CSIRO/Australia (Ziehn et al., 2020) 2 MIROC6 256 * 128 MIROC Consortium (JAMSTEC, AORI, NIES, R-CCS)/Japan (Kataoka et al., 2020) 3 MRI-ESM2-0 320 * 160 MRI, Meteorological Research Institute/Japan (YUKIMOTO et al., 2019) 3.3.2. CMhyd model CMhyd is a software used for simulation, statistical exponentiation and bias correction of climate data at stations located in a watershed. Therefore, for each station, the daily data of the model in question must first be extracted and bias-corrected. The bias correction process corrects the output of climate models at the station level by applying transformation algorithms. Therefore, to detect bias, first, a bias correction algorithm that leads to bias correction of historical data must be identified between the observed and simulated historical climate variables of each model. Then, the identified algorithm is applied to the historical and scenario data to do bias correction of their data and the overestimation or underestimation of these models relative to the observed values. Figure 2 illustrates the methodology of these steps (Rathjens et al., 2016 ). 3.3.3. Bias correction and statistical downscaling With the advancement of science and technology, the use of climate model simulations has gained momentum, but there is still a risk of using these data due to bias. Several bias correction methods are used today, using methods with simple scaling to multi-scale methods, to minimize these biases and increase the accuracy of the simulations. However, it is necessary to use the results of climate model simulations for temperature and especially precipitation with caution. Some of these biases are due to systematic errors in the model itself. However, the use of bias correction techniques helps to minimize these differences. These methods correct the outputs of climate models by applying transformation algorithms in mathematics and statistics. It is assumed that the bias correction algorithm remains constant in current and future climate conditions. There are several bias correction methods for downscaling climate data, including linear scaling, spatial intensity scaling, power transformation, variance scaling, distribution mapping, and delta shift. Furthermore, Rashidi Ghane et al. (2023) proposed the linear scaling (LS) method for projecting and exponential downscaling of precipitation variables among the three downscaling methods (LS, DM, DC) in the study area of ​​this study (Kashafarud catchment). Therefore, in this study, the exponential downscaling and bias correction process was performed using the LS method, a simple and widely used method for bias correction in climate data. In this method, a fixed bias correction coefficient is calculated for each month. This coefficient is obtained by dividing the average value of observations by the average value of model simulations in that month. Then, to correct the simulated values, this coefficient is multiplied by the simulated value. In other words: Bias-corrected value = Simulated value × Bias correction coefficient The primary assumption in the LS method is that the ratio between the simulated values and the observations remains constant over time. In other words, if the model underestimated the average precipitation values by 20% in a certain period, it is expected to have the same error in other periods. 3.3.4. Statistical performance indicators of climate models All climate and hydrological models are associated with uncertainty. Therefore, the results of these models should be interpreted with caution. As mentioned, in this research, two scenarios, SSP1-2.6 and SSP5-8.5, which represent low and high-release scenarios, respectively, were used. To determine the performance of the climate models in simulating precipitation, bias correction was performed using the LS method. Providing climate projections in all three approaches of direct output, bias-corrected, and creating a general model with minimal uncertainty is of particular importance for strategic and management plans. A standard method for reducing model uncertainty is to select a model with the lowest bias value. In this approach, the performance of the model for the historical period is tested concerning station observations and reanalyzed data (Lee & Wang, 2014 ). However, the application of this approach is associated with problems, such as the high model bias in the historical period (Yan et al., 2019 ). Researchers have used various methods to analyze the sensitivity, calibration, and uncertainty of the CMIP6 model. In this study, the statistical indicators of the coefficient of determination (R 2 ), root mean square error (RMSE), and KGE coefficient were used to evaluate the performance of the model results. The relationships of these indicators are shown in equations (1) to (3). Finally, to choose the appropriate model, the downscaled data were evaluated with observational data. The conventional statistical KGE, RMSE, and R 2 methods were employed to verify the performance of the models and compare them. These criteria are calculated based on the following equation. Eq. 1 \(\:R\begin{array}{c}2\\\:\:\end{array}=\:{\left[\frac{\frac{1}{n}\sum\:_{m=1}^{n}\left({s}_{i}-\stackrel{-}{s}\right)\left({o}_{i}-\stackrel{-}{o}\right)\:}{{\sigma\:}_{s}*{\sigma\:}_{o}}\:\right]}^{2}\) Where: \(\:{o}_{i}\) is the observed data, \(\:{s}_{i}\:\) is the estimated data, and σ is the variance. R 2 represents the relationship between the observed and calculated data. The range of this parameter is between zero and one; the closer this value is to one, the stronger the relationship between the two groups (Moriasi et al., 2007 ). Eq. 2 \(\:RMSE=\sqrt{{\frac{1}{n}{\sum\:}_{i=0}^{n}({M}_{i}-{O}_{i})}^{2}}\:\) Where: \(\:{M}_{i}\) and \(\:{O}_{i}\) are the modelled and observed values, respectively. The RMSE ranges from 0 to + 1 and it is used for checking the estimation accuracy between observed and modelled values. A RMSE value close to 0 indicates a higher estimation accuracy. The closer the KGE value is to 1, the better the model’s performance. Eq. 3 \(\:KGE=1-\:\sqrt{{\left(r-1\right)}^{2}\:}+{\left(\frac{{\sigma\:}_{sim}}{{\sigma\:}_{obs}}-1\right)}^{2}+{(\frac{{\mu\:}_{sim}}{{\mu\:}_{obs}}-1)}^{2}\) Where 𝑠𝑖𝑚 and 𝑜𝑏𝑠 are the simulated and observed values, respectively. 𝜎 is the standard deviation, T is the period, 𝜇 is the mean, and r is the linear correlation between the observational data and model data (Zarrin et al., 2021) . 4. Findings To evaluate the performance of global climate models ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 in producing precipitation data, the historical data of these models were compared with the observation data in the base period on a seasonal basis for twelve selected stations in the study Catchment. Table 3 illustrates the R 2 , RMSE, and KGE performance metrics values of the precipitation patterns in different seasons. In Table 4 , these values are illustrated annually. The obtained results demonstrate the performance of the models in simulating the seasonal precipitation of each station. The evaluation of results showed that the MIROC6 and MRI-ESM2-0 models had the best performance, respectively. Finally, the calculated statistics show the better performance of the MIROC6 model compared to the MRI-ESM2-0 and ACCESS-ESM1-5 models in most of the stations of the Kashafrud catchment. According to the obtained results, the precipitation simulation of future periods was done by the MIROC6 climate model. 4.1. Projection of spring precipitation in the near and middle future periods under SSP scenarios The comparison of results of the precipitation values in the base period and the future periods in the spring as the rainiest season show a decrease in the near (2025–2054) and middle (2055–2084) future periods to the base period (1991–2020) in most stations. The lowest precipitation estimates in the near future period of 2025-54 has been made in the SSP1_2.6 scenario compared to other scenarios. Besides, under the middle future of the SSP5-8.5 scenario and the near future of the SSP5-8.5 scenario, an increase in precipitation will occur in some stations such as Andorokh, Golestan, Mashhad, and Sarassiab compared to the observation period. Generally, the average precipitation of the study stations in the spring season under the SSP1_2.6 scenario in the near future period is an 8.18% decrease in precipitation. Under the SSP5_8.5 scenario in the middle future, average precipitation of the stations decreases by 1.51%, and under the SSP1_2.6 and SSP5_8.5 scenarios in the near future (2025-54) and the middle future (2055-84), decreases by 3.08% respectively and increases by 3.56%. The details have shown if Fig. 3 . In general, climate change can have a significant impact on precipitation patterns in the study area, and the decrease in precipitation in the spring season, especially in the SSP1_2.6 scenario, is one of these impacts. Given that precipitation changes are projected to increase at some stations and decrease at others, it is essential to note that different SSPs show different scenarios of future changes, which may change the general circulation patterns of the atmosphere in a way that increases precipitation at some stations and decreases at others under the influence of different precipitation systems, temperature, wind, topography, and vegetation. The difference in precipitation projection in different stations shows the complexity of climate changes and the influence of local factors on precipitation patterns. 4.2. Projection of summer precipitation in the near and middle future periods under SSP scenarios Before examining the results, it is necessary to point out that the precipitation of this region occurs much less in the summer season than in other seasons, to the point where some studies avoid comparing the results of this season. It is necessary to consider this issue when using the results obtained. Considering this point, the examining of results of the changes in precipitation of the stations of the study area in the summer shows a decrease in the average precipitation values in the stations of Andorokh, Dowlatabad, Mashhad, Olang-e Asadi, and Sarasiab. However, in other stations, an increase in the average precipitation values in most periods of the near and middle future is evident. In general, the average precipitation of the stations showed that in the SSP1_2.6 scenarios, the near future (2025-54) and middle future (2055-84) periods increased by 3.05% and 16.59%, and in the SSP5_8.5 scenarios, the near future (2025-54) and middle future (2055-84) periods increased by 12.25% and 33.57%, respectively, compared to the same observation period (1991–2020). The details illustrate in Fig. 4 . It should be noted that the number 33.57% corresponds to the average precipitation of the stations in the study area under the SSP5_8.5 scenario, the far future period, in the summer season, as the least rainy season in the study area. Table 5 shows more details. 4.3. Projection of autumn precipitation in the near and middle future periods under SSP scenarios The comparison of precipitation values in the base period and the future periods of the autumn season shows a decrease in most stations (Andorokh, Dawlatabad, Jaghargh, Mashhad, Radkan, and Sarassiab) in all scenarios. On average, in the study area in the autumn, under the SSP1_2.6 and SSP5_8.5 scenarios of the near future period, average precipitation decreases by 3.26% and 2.03%, respectively. Moreover, in the SSP1_2.6 and SSP5_8.5 scenarios, in the middle future, average precipitation was observed as 3.68 and 2.09%, respectively. Figure 5 illustrates the projection of autumn precipitation in future. 4.4. Projection of winter precipitation in the near and middle future periods under SSP scenarios Winter is the rainiest season in the study area. According to the results, in almost all stations, the winter precipitation values show an increase in the middle future period under the SSP5_8.5 scenario than other scenarios. This issue was also evident in other seasons. Also, the lowest percentage of precipitation increase was observed in the near future period under the SSP1_2.6 scenario. Except for the SSP5_8.5 scenario of the middle future period, which shows an increase in precipitation in some stations, a significant decrease in rainfall was observed in almost all other scenarios in the winter season compared to the base period. In general, examining the average percentage of precipitation changes in stations shows an increase between 6.75% in the SSP1_2.6 scenario of the near future period to a rise of 20.96% in the SSP5_8.5 scenario in the middle future period. Figure 6 illustrates the projection of autumn precipitation in future. The reason for this increase can be various mechanisms, including changes in pressure and temperature patterns, and as a result, changing path of precipitation systems and an increase in precipitation in the winter seasons in the study area. 4.5. Projection of annual precipitation in the near and middle future periods under SSP scenarios As Fig. 7 depicts, Androkh, Dowlatabad, Jaghargh, Olang-e Asadi, Radkan, and Sarasiab stations show a decrease in precipitation in all scenarios of the future period compared to the base period. In other stations, a certain increase in the percentage of precipitation in each future period has been observed in different scenarios, the most significant increase being related to the pessimistic scenario of the middle future period. Also, in the SSP1_2.6 scenario, the near future period shows a smaller increase than the SSP5_8.5 scenario. In general, the mean annual precipitation of the stations in the study area shows a decrease of 2.92% and a rise of 3.44%, respectively, in the SSP1_2.6 and SSP5_8.5 scenarios of the near future period and an increase of 2.96 and 8.79%, in the SSP1_2.6 and SSP5_8.5 scenarios of the middle future period. 4.6. Intercomparison of seasonal precipitation changes Figure 8 shows the seasonal and annual precipitation changes in the study area, and Table 5 shows the variability percentage of seasonal and annual precipitation. According to the available results, all the scenarios except the SSP5-8.5 scenario show a decrease in precipitation in the spring season. The summer, the least rainy season in the study area, showed almost the same results as the base period with a slight increase. In the autumn, all scenarios show a slight decrease compared to the base period. In the winter, all scenarios show an increase in precipitation, which is a higher percentage in the middle future, SSP5_8.5 scenario. The lowest increase in the winter season is related to the near future period under SSP1_2.6 scenario. 5. Conclusion The results of the CMhyd model evaluation based on R2, RMSE, and KGE criteria showed that in the study area, the simulation was performed more accurately by the MIROC6 model. The mean of KGE and RMSE coefficients for precipitation were 0.91 and 21.09, respectively. The mean annual rainfall under the SSP1_2.6 and SSP5_8.5 scenarios in the near future (2025–2054) decreased by -2.92 and increased by 3.44 percent, respectively, and under the SSP1_2.6 and SSP5_8.5 scenarios in the middle future (2055–2084) increased by 2.96% and 8.79%, respectively, compared to the observation period. In terms of seasonal comparison, in most scenarios of future periods, a decrease in precipitation between 1.51% and 8.18% was observed in the spring and autumn seasons, and only in the SSP5_8.5 scenario of the distant future period (2055-84), there was an increase in precipitation in the spring season by 3.56%. In the summer and winter seasons, in all scenarios, an increase in rainfall between 3.05 and 33.57% was observed. According to the available results, all scenarios except the SSP5_8.5 scenario show a decrease in precipitation in the spring season in the middle future. The summer season, which is the least rainy season in the study area, showed a slight increase in the precipitation for future periods. In the autumn, all scenarios show a slight decrease compared to the base period. In the winter season, it shows an increase in precipitation in all scenarios, which in the SSP5_8.5 scenario has a higher percentage, which may be due to the rise in the intensity of extreme precipitation events that can lead to flooding and damage. These changes reflect the complex nature of climate change and its varying impacts on different regions. A comparison of various studies conducted based on different models of the IPCC Sixth Assessment Report (Afsari et al., 2024 ; Babaeian et al., 2023 ; ShiftehSome’e et al., 2012; Zenozi Alamdari et al., 2025) showed that the results of some of the studies conducted are consistent with the results of this study. The results of this research can be used as a management tool for planning and decision markers to deal with the impacts of climate change on the water resources of the Kashafrud catchment, which is necessary to determine the impact of increasing or decreasing precipitation on other hydrological parameters such as runoff, evaporation, and soil permeability should also be checked. Increasing precipitation in the winter season can help improve water resources, but on the other hand, increasing heavy precipitation events and changing seasonal patterns create a new challenge for water resources management. Therefore, in addition to adapting to these changes, investing in catchment infrastructure, improving irrigation systems, and increasing the efficiency of water consumption is necessary to ensure water security in the future. Declarations Declarations Conflict of Interest: The authors have no conflicts of interest to declare that they are relevant to the content of this article. Ethics approval : Not Applicable Consent to participate: Not Applicable Consent for publication: All Authors consent to the article's publication after acceptance. Funding Statement: The authors did not receive support from any organisation for the submitted work. Author Contribution All authors contributed to the study's conception and design. Hossain Imanipour and Mokhtar Karami performed material preparation, data collection, and analysis carried out by Mokhtar Karami and Abdolreza Kashki. Hossain Imanipour and Morteza Esmailnejad wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Afsari, R., Nazari-Sharabian, M., Hosseini, A. & Karakouzian, M. A Cmip6 Multi-Model Analysis of the Impact of Climate Change on Severe Meteorological Droughts through Multiple Drought Indices—Case Study of Iran’s Metropolises. Water 16 (5), 711 (2024). https://www.mdpi.com/2073-4441/16/5/711 Babaeian, I. et al. Projection of Iran’s Precipitation in 21st Century Using Downscaling of Selected Cmip6 Models by Cmhyd. Earth Space Phys. 49 (2), 431–449. https://doi.org/http//doi.org/10.22059/jesphys.2023.332410.1007436 (2023). Baker, N. C. & Huang, H. P. A Comparative Study of Precipitation and Evaporation between Cmip3 and Cmip5 Climate Model Ensembles in Semiarid Regions. J. Clim. 27 (10), 3731–3749 (2014). Elguindi, N. & Giorgi, F. Simulating Multi-Decadal Variability of Caspian Sea Level Changes Using Regional Climate Model Outputs. Clim. Dyn. 26 , 167–181 (2006). Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (Cmip6) Experimental Design and Organization. Geosci. Model Dev. 9 (5), 1937–1958 (2016). Guo, J. et al. The Response of Warm-Season Precipitation Extremes in China to Global Warming: An Observational Perspective from Radiosonde Measurements. Clim. Dyn. 54 , 3977–3989 (2020). Kamal, A. & MassahBavani, A. Comparison of Future Uncertainty of Aogcm-Tar and Aogcm-Ar4 Models in the Projection of Runoff Basin. Journal Earth Space Physics 3 (2012). Kim, H. et al. Spatial Assessment of Water-Use Vulnerability under Future Climate and Socioeconomic Scenarios within a River Basin. J. Water Resour. Plan. Manag. 146 (7), 05020011 (2020). Lee, J. Y. & Wang, B. Future Change of Global Monsoon in the Cmip5. Clim. Dyn. 42 (1), 101–119 (2014). Moriasi, D. N. et al. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE . 50 (3), 885–900 (2007). Nikakhtar, M., Rahmati, S. H., Babaeian, I. & AliReza MassahBavani, and Mitigating the Adverse Impacts of Climate Change on River Water Quality through Adaptation Strategies: A Case Study of the Ardak Catchment, Northeast Iran. Theoret. Appl. Climatol. https://doi.org/https://doi.org/10.1007/s00704-024-05057-8 (2024). Papalexiou, S. M. & Montanari, A. Global and Regional Increase of Precipitation Extremes under Global Warming. Water Resour. Res. 55 (6), 4901–4914 (2019). Raghavan, S. V., Jiandong Liu, N. S., Nguyen, M. T. & Vu Assessment of Cmip5 Historical Simulations of Rainfall over Southeast Asia. Theoret. Appl. Climatol. 132 , 989–1002 (2018). RashidiGhane, M., Mottoli, S., JanbazGhobadi, G. & Kohi, M. Evaluating the Capability of Three Statistical Methods of Micro-Scale Scaling of Cmip6 Models Temperature and Precipitation Output in the Kashf Rood Watershed. Climatology research no. 53 (2023): 117 – 32. (2023). Rathjens, H., Bieger, K., Srinivasan, R., Chaubey, I. & Arnold, J. Documentation for Preparing Simulated Climate Change Data for Hydrologic Impact Studies. URL :http://swat.tamu.edu/software/cmhyd (2016). 42 Rivera, J. A. & Arnould, G. Evaluation of the Ability of Cmip6 Models to Simulate Precipitation over Southwestern South America: Climatic Features and Long-Term Trends (1901–2014). Atmos. Res. 241 , 104953 (2020). ShiftehSome'e, B., Ezani, A. & Tabari, H. Spatiotemporal Trends and Change Point of Precipitation in Iran. Atmos. Res. 113 , 1–12 (2012). Sun, Y., Solomon, S., Dai, A., Robert, W. & Portmann How Often Does It Rain? J. Clim. 19 (6), 916–934 (2006). Wang, B., Kim, H. J., Kikuchi, K. & Kitoh, A. Diagnostic Metrics for Evaluation of Annual and Diurnal Cycles. Clim. Dyn. 37 , 941–955 (2011). Yan, Y., Lu, R. & Li, C. Relationship between the Future Projections of Sahel Rainfall and the Simulation Biases of Present South Asian and Western North Pacific Rainfall in Summer. J. Clim. 32 (4), 1327–1343 (2019). Zamani, Y., Monfared, S. A. H., Moghaddam, M. A. & Hamidianpour, M. A Comparison of Cmip6 and Cmip5 Projections for Precipitation to Observational Data: The Case of Northeastern Iran. Theoret. Appl. Climatol. 142 , 1613–1623 (2020). Zarrin, A., Salehabadi, N. & abbasali dadashi-rodbari, and Projected Temperature Anomalies and Trends in Different Climate Zones in Iran Based on Cmip6. Iran. J. Geophys. 15 (1), 35–54. https://doi.org/10.30499/ijg.2020.249997.1292 (2021). https://www.ijgeophysics.ir/article_118940_6cdfc81d4bdd585b315311c1de0b00c6.pdf Zarrin, A. Projected Changes in Temperature over Iran by 2040 Based on Cmip6 Multi-Model Ensemble. Phys. Geogr. Res. Q. 53 (1), 75–90. https://doi.org/10.22059/jphgr.2021.308361.1007551 (2021). https://jphgr.ut.ac.ir/article_81103_899b830b235a5bf6243b7f5ab4387548.pdf Zazulie, N., Rusticucci, M., Graciela, B. & Raga Regional Climate of the Subtropical Central Andes Using High-Resolution Cmip5 Models. Part Ii: Future Projections for the Twenty-First Century. Clim. Dyn. 51 , 2913–2925 (2018). Zenozi Alamdari, Nazli, B., Sobhani, M., Eshahi & Mohammadi, M. Precipitation and Temperature Zoning of Khorasan Razavi Province Using Data from the Sixth Climate Change Report (Cmip6). J. Environ. Sci. Stud. 9 (4), 9761–9753 (2025). Tables Tables 3 to 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables3To5.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5953463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":413143958,"identity":"97e9ee72-56ca-43c4-915f-04eaaab4ce60","order_by":0,"name":"Hossain Imanipour","email":"","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":false,"prefix":"","firstName":"Hossain","middleName":"","lastName":"Imanipour","suffix":""},{"id":413143959,"identity":"bfbbbf23-02e2-40f6-96e1-53ff3b5a43e3","order_by":1,"name":"Mokhtar Karami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACgwM8BiCah429+QADYwNM/ABuLZYHeBLAWvh5jiUQp8X+AA9EVnJGjgFxWswO8DZ+rqjZJmNw5sw3iZ87bOQY2A8fYOY5g08LP7PkmWO3eQyO926T7D2TZszAk5bAzHMDrxYGyQY2oJYzZ7dJ8LYdTmyQ4DFg5vmAW4vBAd7mnw3/gFpu5DyT/EucFp5jko1tt3mA3meTRtiCx2EGh3nSLBv7boMC2dhati3NmA3ol4Nz8Hjf4HiP8c2Gb7ftgVH58ObbNhs5fvbDBx+8OYZbCwMzgskiASLZGPDGCppuPH4eBaNgFIyCkQwA/AJWezWLtr0AAAAASUVORK5CYII=","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":true,"prefix":"","firstName":"Mokhtar","middleName":"","lastName":"Karami","suffix":""},{"id":413143960,"identity":"43403236-4e3c-4c0f-a81c-87c38b6f14f4","order_by":2,"name":"Abdolreza Kashki","email":"","orcid":"","institution":"Hakim Sabzevari University","correspondingAuthor":false,"prefix":"","firstName":"Abdolreza","middleName":"","lastName":"Kashki","suffix":""},{"id":413143961,"identity":"86632ca1-9a5c-4be5-ace5-6571ef5826fe","order_by":3,"name":"Morteza Esmailnejad","email":"","orcid":"","institution":"University of Birjand","correspondingAuthor":false,"prefix":"","firstName":"Morteza","middleName":"","lastName":"Esmailnejad","suffix":""}],"badges":[],"createdAt":"2025-02-03 19:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5953463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5953463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75996869,"identity":"4be9b740-7f57-4b15-ac19-aa18f429fd1c","added_by":"auto","created_at":"2025-02-11 09:54:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253937,"visible":true,"origin":"","legend":"\u003cp\u003eThe geographic location of the study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/790804ada4c176a6eee747f8.png"},{"id":75996868,"identity":"ff7130f1-934b-4107-a342-6c89dcabdd23","added_by":"auto","created_at":"2025-02-11 09:54:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88531,"visible":true,"origin":"","legend":"\u003cp\u003eBias correction framework(Rathjens et al., 2016)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/57b3e6d3b82d8fa2cd1f6011.png"},{"id":75996867,"identity":"c291e953-05d6-4962-b88b-4b61984c2e38","added_by":"auto","created_at":"2025-02-11 09:54:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32637,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of spring precipitation in the near and middle future periods under SSP scenarios\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/88d2429fc80912d297205357.png"},{"id":75996906,"identity":"3c6171ab-107b-4e4d-903e-71b4dca6417d","added_by":"auto","created_at":"2025-02-11 09:54:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30011,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of summer precipitation in the near and middle future periods under SSP scenarios\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/89d767500e3c022534e13fb3.png"},{"id":75998930,"identity":"2a8fc936-685a-460e-acd1-400b23069962","added_by":"auto","created_at":"2025-02-11 10:10:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32477,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of autumn precipitation in the near and middle future periods under the SSP scenarios\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/001cd053e2e991b30c7f38ba.png"},{"id":75998931,"identity":"d06958db-bc77-46c7-8704-5bb5effbc249","added_by":"auto","created_at":"2025-02-11 10:10:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":25644,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of winter precipitation in the near and middle future periods under SSP scenarios\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/bac9caeecc570cad1e958488.png"},{"id":75997397,"identity":"99aa7c6c-747a-44be-b7e6-bdb6d6d33747","added_by":"auto","created_at":"2025-02-11 10:02:54","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":28042,"visible":true,"origin":"","legend":"\u003cp\u003eProjection of annual precipitation in the near and far future periods under SSP scenarios\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/81002d38757098f1015bdc76.png"},{"id":75997400,"identity":"1c0f3151-3735-4d8e-9112-42da7ae4b7eb","added_by":"auto","created_at":"2025-02-11 10:02:54","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":24163,"visible":true,"origin":"","legend":"\u003cp\u003eComparing the seasonal precipitation in the base period with the near and middle future periods under the SSP1_2.6 and SSP5_8.5 scenarios\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/89641bdce9e807db6df7fed0.png"},{"id":77875377,"identity":"291fea53-2c4b-4c94-b25e-f6265e6d6376","added_by":"auto","created_at":"2025-03-06 11:08:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1458163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/db1d2f9c-6c0e-43c0-a461-7813cc065fb7.pdf"},{"id":75996866,"identity":"aa8cecdd-6d73-40dd-a24f-9b419beca47c","added_by":"auto","created_at":"2025-02-11 09:54:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34229,"visible":true,"origin":"","legend":"","description":"","filename":"Tables3To5.docx","url":"https://assets-eu.researchsquare.com/files/rs-5953463/v1/2b85571c264469997a2cdd47.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Projecting the impacts of future climate change on precipitation in the Kashafrud catchment in Iran","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe excessive use of fossil fuels, land use change, the increase in the world\u0026rsquo;s population, and consequently, the increasing expansion of industrial activities to provide the welfare and needs of the population of the Earth have caused visible changes in the climate of the Planet gradually after the industrial revolution. The most obvious of these changes is the increase in the average temperature of the Earth and the increase in extreme climatic phenomena such as floods, storms, hail, tropical storms, heat waves, rising sea levels, melting of polar ice, and drought. The increase of these events in recent years has become the primary concern of climatologists and heads of countries of the world. The attention of public opinion and scientific societies to this issue and the concentration of greenhouse gases in the past few years has led to a kind of global cooperation in the investigation of this global problem because research has revealed that the temperature anomalies in many different parts of the globe Environmental issues such as floods, storms, and droughts are followed. One of the most important of these changes is the change in precipitation. Determining the seasonal or monthly fluctuations of precipitation can inform managers and planners in different departments and provide them with an accurate picture of future climate changes and fluctuations so that decisions can be made according to the upcoming weather conditions. Precipitation is a fundamental feature of the Earth\u0026rsquo;s hydrological cycle and can have many impacts on various human activities (Sun et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the change in the precipitation pattern, the intervals of precipitation have also changed and caused floods in some areas. The impact of climate change on the aggravation of drought is considered a more dominant phenomenon in different regions of the world (Wang et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Climate change is expected to change the magnitude and spatial and temporal patterns of hydro-climatic variables, such as precipitation (Guo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Papalexiou \u0026amp; Montanari, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accordingly, evaluating the performance of existing models and specifying uncertainties and underlying biases in climate model simulations are essential to understand their value and potential for doing studies on climate change impact assessment (Raghavan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rivera \u0026amp; Arnould, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zazulie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Recent estimates of global warming indicate the accuracy of the Coupled Model Intercomparison Project (CMIP) data, which has led to an increase in confidence in the accuracy of the project (Baker \u0026amp; Huang, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Despite conducting numerous studies in the field of climate change and its impacts on different economic sectors by applying the outputs of various climate change models, using CMIP6 series models (Pbt a,n) and Shared Socioeconomic Pathways (SSP) scenarios of the Intergovernmental Panel on Climate Change (IPCC) around the world, so far, only a handful of studies with selected models. The models used in this research include ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 models in the Kashafrud catchment.\u003c/p\u003e \u003cp\u003eConsidering the vulnerability of different regions to climate change, it is imperative and necessary to study climate change based on the data and report of the sixth phase to investigate the impacts of climate change to adopt codified policies and plans and policies in various sectors. The purpose of this research is to project the impact of future climate change on the precipitation of the Kashafrud catchment. The results of this research can be used in the long-term planning of the agricultural and water resources management sectors and industry. Also, presenting these results can be very helpful in compiling upstream documents. The results of the climate models under the CMIP6 project scenarios promise to improve and strengthen the climate change project information for countries. In this regard, much research using different CMIP6 scenarios is being conducted in the world.\u003c/p\u003e"},{"header":"2. Review of literature","content":"\u003cp\u003eIn the past years, scientists and experts in geography and climatology have conducted extensive research on the impacts of climate change on precipitation and have tried to identify and explain the relationship between climate elements and factors by presenting different methods. Elguindi and Giorgi (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) investigated the response of the sea level of Mazandaran to climate change for the years 1948 to 1990 using the Climate Model data for hydrologic modeling (CMhyd). They projected the changes in sea level height with the help of a simple hydrological equation. In their research, it was proved that this hydrological model has correctly projected the real changes in the sea level. Eyring et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reviewed the design of the experiments and the structure of the CMIP6 models. Through the design and distribution of simulations of global climate models, the CMIP project has become one of the fundamental pillars of climatology. Kim et al (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) compared the SSP and Representative Concentration Pathways (RCP) Scenarios in a study in the Han River catchment of South Korea. Their results have shown that the results obtained from the SSP scenarios with social and economic conditions attached to them are very different from the previous scenarios, including RCP. Therefore, this issue shows the importance of socioeconomic factors in climate change.\u003c/p\u003e \u003cp\u003eZamani et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) attempted to evaluate the performance of the CMIP5 and CMIP6 models in projecting the average precipitation on an annual and seasonal time scale in the north and northwest of Iran in the 1987\u0026ndash;2005 period using statistics such as relative coefficient, correlation coefficient, Root Mean Square Error (RMSE), and relative error did. Their study is considered the first attempt to compare data from these two projection models in this region. The results showed that the simulated precipitation of the two projection models was different. Also, the relative bias for winter in all stations in CMIP6 models was lower than CMIP5, which indicates the better performance of the mentioned models, and in general, CMIP6 models had better performance than CMIP5.\u003c/p\u003e \u003cp\u003eIn a study titled \u0026ldquo;Precipitation and temperature zoning of Razavi Khorasan province using data from the sixth climate change report (CMIP6)\u0026rdquo;, Alamdari et al. (2024) examined the impacts of climate change on precipitation and temperature of 7 synoptic stations of Razavi Khorasan province, including the Mashhad station. Their results showed that in all scenarios, annual precipitation will experience an increase of 0.4 to 6.8%. Given the increased accuracy of CMIP6 models, it is necessary to visualize the future climate conditions of different locations based on the results of these models and to use the most complete of these new models. For this purpose, in the present study, the results of the latest available CMIP6 report and Earth System Model (ESM), which consider more components of the Earth system in climate modelling and have better capabilities compared to General Circulation Models (GCM) and also support leap years, were used. Given that several studies indicate the remarkable performance of various downscaling methods and models, including the CMhyd model, in projecting climate change patterns at the local scale, therefore, in this study, the CMhyd model was considered for simulating future climate data. In the following sections, the research method, study area and data, the performance of selected CMIP6 models, and the results and discussion are discussed.\u003c/p\u003e"},{"header":"3. Data and methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study area\u003c/h2\u003e \u003cp\u003eThe study area is the upstream part of the Kashafrud catchment as part of the Qara-Qom basin. The Kashafrud catchment is located in the northeast of Iran, between the longitudes of 58\u0026ordm; and 2\u0026rsquo; to 59 \u0026ordm; and 45\u0026rsquo; east and the latitudes of 36 \u0026ordm; and 3\u0026rsquo; to 37 \u0026ordm; and 20\u0026rsquo; north (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total upstream of ​​the Kashafrud catchment is about 8000 km\u003csup\u003e2\u003c/sup\u003e, of which 2000 km\u003csup\u003e2\u003c/sup\u003e are plains, and the rest are highlands. The minimum and maximum elevations of the Catchment are 852 and 3309 meters, respectively. The existence of different elevations and the proximity of the Kashafrud catchment to the central desert play a significant role in the climatic changes of this region and have caused climate diversity in it.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Observational climate data\u003c/h2\u003e \u003cp\u003eIt is necessary to compare its historical data with the station\u0026rsquo;s observational datasets using standard methods, which we will describe in detail below to compare and determine the performance of the models in the projecting process. Therefore, for the study area, the observational daily precipitation data of 12 weather stations from 1991 to 2020 were obtained from Razavi Khorasan Regional Water Corporation and Iran Meteorological Office, Iran. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the characteristics of the selected stations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e) Characteristics of the selected upstream stations of the Kashafrud catchment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteorological station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLongitude (D.D.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLatitude (D.D.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElevation (meter)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAndrokh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eMulti-parameter rain-gauge stations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArdak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDowlatabad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGolestan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJaghargh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKardeh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlang-e Asadi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadkan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSarasiab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMashhad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSynoptic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e999.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGolmakan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean precipitation values of the 12 weather stations in the near future (2025-54) and middle future (2055-84) periods were simulated via CMhyd software under the selected scenarios. For this purpose, the output of climate models was extracted for all stations, and bias corrected. Then, the algorithm was applied to the historical and scenario data, and their data was modified, and the overestimation or underestimation of these data was corrected compared to the observed values. It is also necessary to apply statistical coefficients to estimate the accuracy of the model before using the results of each model. One of the necessities in the studies related to climate change is to check the quality control and homogeneity of the data pertaining to the stations in the study area. In this respect, cases such as the lack of statistics for precipitation data, the completeness and accuracy of the days of the months, and the check of outlier data were investigated. After performing the quality control of observational datasets, data homogeneity tests and homogenization of heterogeneous time series of precipitation were performed. Finally, the Linear scaling (LS) method was used to do the bias correction of the precipitation data, and the accuracy of the simulation of the precipitation variable in different stations of the study area was calculated using statistical criteria. Also, previous studies conducted in some stations of the study area (Rashidi Ghane et al., 2023) have introduced the LS method to do the bias correction of the precipitation variable in this area, which was also used in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Climate change models and scenarios\u003c/h2\u003e \u003cp\u003eOne of the main factors in the downscaling of micro-scale climate data is the screening of climate change models.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Historical climate data\u003c/h2\u003e \u003cp\u003eHistorical climate data cover the period in which the observational datasets are available. Climate data verification is performed by comparing the historical climate data with observational data. At present, the period covered by the model used in this research is from 1991 to 2014.\u003c/p\u003e \u003cp\u003eThe use of SSP scenarios, which are a combination of RCP scenarios and socioeconomic policies, is one of the crucial differences between CMIP6 models compared to previous models. To run the CMhyd model, daily precipitation data is required. Therefore, the output of the selected models under the SSP1-2.6 and SSP5-8.5 scenarios for the Kashafrud catchment was obtained, after examining the error rate using different statistics, and exponential downscaling was performed using the CMhyd model.\u003c/p\u003e \u003cp\u003eOne of the major limitations in using the output of GCM models is their low spatial resolution, which does not correspond to the accuracy required by hydrological models in terms of space and time. Therefore, downscaling methods are used to overcome this limitation (Kamal \u0026amp; Massah Bavani, 2012). Considering similar research in the study area, ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 models, which were introduced and used in other studies (Babaeian et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nikakhtar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zarrin \u0026amp; Dadashi-Roudbari, 2021) as models with better performance in this area, were used. The difference between this research from other research conducted in the study area is the use of a more significant number of stations, a different downscaling model, and the use of selected and proposed climate models. Next, precipitation values ​​were selected and applied using three CMIP6 ESM models, which, in addition to their initial screening in terms of climate model type (GCM or ESM), were selected and applied based on the availability of daily data, historical and future data, and the presence of temperature and precipitation data in two SSP1-2.6 and SSP5-8.5 scenarios, which represent low and high emission scenarios, respectively, in the study area. The projection by these models is a combination of a new set of emission and land use scenarios obtained by LAMs models based on the Shared Socioeconomic Pathways (SSPs) in the future period (including age, economic growth, urbanization, education, and population) and relation to greenhouse gas concentration scenarios (Eyring et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the characteristics of CMIP6 models.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of CMIP6 models used in this research\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003cp\u003ename\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorizontal\u003c/p\u003e \u003cp\u003eresolution (Km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitution/Country\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192 * 144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCSIRO/Australia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Ziehn et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256 * 128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMIROC Consortium (JAMSTEC, AORI, NIES, R-CCS)/Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Kataoka et al., 2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320 * 160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMRI, Meteorological Research Institute/Japan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(YUKIMOTO et al., 2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. CMhyd model\u003c/h2\u003e \u003cp\u003eCMhyd is a software used for simulation, statistical exponentiation and bias correction of climate data at stations located in a watershed. Therefore, for each station, the daily data of the model in question must first be extracted and bias-corrected. The bias correction process corrects the output of climate models at the station level by applying transformation algorithms. Therefore, to detect bias, first, a bias correction algorithm that leads to bias correction of historical data must be identified between the observed and simulated historical climate variables of each model. Then, the identified algorithm is applied to the historical and scenario data to do bias correction of their data and the overestimation or underestimation of these models relative to the observed values. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the methodology of these steps (Rathjens et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Bias correction and statistical downscaling\u003c/h2\u003e \u003cp\u003eWith the advancement of science and technology, the use of climate model simulations has gained momentum, but there is still a risk of using these data due to bias. Several bias correction methods are used today, using methods with simple scaling to multi-scale methods, to minimize these biases and increase the accuracy of the simulations. However, it is necessary to use the results of climate model simulations for temperature and especially precipitation with caution. Some of these biases are due to systematic errors in the model itself. However, the use of bias correction techniques helps to minimize these differences. These methods correct the outputs of climate models by applying transformation algorithms in mathematics and statistics. It is assumed that the bias correction algorithm remains constant in current and future climate conditions. There are several bias correction methods for downscaling climate data, including linear scaling, spatial intensity scaling, power transformation, variance scaling, distribution mapping, and delta shift.\u003c/p\u003e \u003cp\u003eFurthermore, Rashidi Ghane et al. (2023) proposed the linear scaling (LS) method for projecting and exponential downscaling of precipitation variables among the three downscaling methods (LS, DM, DC) in the study area of ​​this study (Kashafarud catchment). Therefore, in this study, the exponential downscaling and bias correction process was performed using the LS method, a simple and widely used method for bias correction in climate data. In this method, a fixed bias correction coefficient is calculated for each month. This coefficient is obtained by dividing the average value of observations by the average value of model simulations in that month. Then, to correct the simulated values, this coefficient is multiplied by the simulated value. In other words:\u003c/p\u003e \u003cp\u003eBias-corrected value\u0026thinsp;=\u0026thinsp;Simulated value \u0026times; Bias correction coefficient\u003c/p\u003e \u003cp\u003eThe primary assumption in the LS method is that the ratio between the simulated values and the observations remains constant over time. In other words, if the model underestimated the average precipitation values by 20% in a certain period, it is expected to have the same error in other periods.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Statistical performance indicators of climate models\u003c/h2\u003e \u003cp\u003eAll climate and hydrological models are associated with uncertainty. Therefore, the results of these models should be interpreted with caution. As mentioned, in this research, two scenarios, SSP1-2.6 and SSP5-8.5, which represent low and high-release scenarios, respectively, were used. To determine the performance of the climate models in simulating precipitation, bias correction was performed using the LS method. Providing climate projections in all three approaches of direct output, bias-corrected, and creating a general model with minimal uncertainty is of particular importance for strategic and management plans. A standard method for reducing model uncertainty is to select a model with the lowest bias value. In this approach, the performance of the model for the historical period is tested concerning station observations and reanalyzed data (Lee \u0026amp; Wang, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the application of this approach is associated with problems, such as the high model bias in the historical period (Yan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Researchers have used various methods to analyze the sensitivity, calibration, and uncertainty of the CMIP6 model. In this study, the statistical indicators of the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), root mean square error (RMSE), and KGE coefficient were used to evaluate the performance of the model results. The relationships of these indicators are shown in equations (1) to (3). Finally, to choose the appropriate model, the downscaled data were evaluated with observational data. The conventional statistical KGE, RMSE, and R\u003csup\u003e2\u003c/sup\u003e methods were employed to verify the performance of the models and compare them. These criteria are calculated based on the following equation.\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;1 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\\begin{array}{c}2\\\\\\:\\:\\end{array}=\\:{\\left[\\frac{\\frac{1}{n}\\sum\\:_{m=1}^{n}\\left({s}_{i}-\\stackrel{-}{s}\\right)\\left({o}_{i}-\\stackrel{-}{o}\\right)\\:}{{\\sigma\\:}_{s}*{\\sigma\\:}_{o}}\\:\\right]}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{o}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the observed data, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{i}\\:\\)\u003c/span\u003e\u003c/span\u003eis the estimated data, and σ is the variance. R\u003csup\u003e2\u003c/sup\u003e represents the relationship between the observed and calculated data. The range of this parameter is between zero and one; the closer this value is to one, the stronger the relationship between the two groups (Moriasi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:RMSE=\\sqrt{{\\frac{1}{n}{\\sum\\:}_{i=0}^{n}({M}_{i}-{O}_{i})}^{2}}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{O}_{i}\\)\u003c/span\u003e\u003c/span\u003e are the modelled and observed values, respectively. The RMSE ranges from 0 to +\u0026thinsp;1 and it is used for checking the estimation accuracy between observed and modelled values. A RMSE value close to 0 indicates a higher estimation accuracy. The closer the KGE value is to 1, the better the model\u0026rsquo;s performance.\u003c/p\u003e \u003cp\u003eEq.\u0026nbsp;3 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:KGE=1-\\:\\sqrt{{\\left(r-1\\right)}^{2}\\:}+{\\left(\\frac{{\\sigma\\:}_{sim}}{{\\sigma\\:}_{obs}}-1\\right)}^{2}+{(\\frac{{\\mu\\:}_{sim}}{{\\mu\\:}_{obs}}-1)}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026#119904;\u0026#119894;\u0026#119898; and \u0026#119900;\u0026#119887;\u0026#119904; are the simulated and observed values, respectively. \u0026#120590; is the standard deviation, T is the period, \u0026#120583; is the mean, and r is the linear correlation between the observational data and model data (Zarrin et al., 2021) .\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Findings","content":"\u003cp\u003eTo evaluate the performance of global climate models ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 in producing precipitation data, the historical data of these models were compared with the observation data in the base period on a seasonal basis for twelve selected stations in the study Catchment. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the R\u003csup\u003e2\u003c/sup\u003e, RMSE, and KGE performance metrics values of the precipitation patterns in different seasons. In Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, these values are illustrated annually. The obtained results demonstrate the performance of the models in simulating the seasonal precipitation of each station.\u003c/p\u003e\n\u003cp\u003eThe evaluation of results showed that the MIROC6 and MRI-ESM2-0 models had the best performance, respectively. Finally, the calculated statistics show the better performance of the MIROC6 model compared to the MRI-ESM2-0 and ACCESS-ESM1-5 models in most of the stations of the Kashafrud catchment. According to the obtained results, the precipitation simulation of future periods was done by the MIROC6 climate model.\u003c/p\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Projection of spring precipitation in the near and middle future periods under SSP scenarios\u003c/h2\u003e\n \u003cp\u003eThe comparison of results of the precipitation values in the base period and the future periods in the spring as the rainiest season show a decrease in the near (2025\u0026ndash;2054) and middle (2055\u0026ndash;2084) future periods to the base period (1991\u0026ndash;2020) in most stations. The lowest precipitation estimates in the near future period of 2025-54 has been made in the SSP1_2.6 scenario compared to other scenarios. Besides, under the middle future of the SSP5-8.5 scenario and the near future of the SSP5-8.5 scenario, an increase in precipitation will occur in some stations such as Andorokh, Golestan, Mashhad, and Sarassiab compared to the observation period.\u003c/p\u003e\n \u003cp\u003eGenerally, the average precipitation of the study stations in the spring season under the SSP1_2.6 scenario in the near future period is an 8.18% decrease in precipitation. Under the SSP5_8.5 scenario in the middle future, average precipitation of the stations decreases by 1.51%, and under the SSP1_2.6 and SSP5_8.5 scenarios in the near future (2025-54) and the middle future (2055-84), decreases by 3.08% respectively and increases by 3.56%. The details have shown if Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. In general, climate change can have a significant impact on precipitation patterns in the study area, and the decrease in precipitation in the spring season, especially in the SSP1_2.6 scenario, is one of these impacts. Given that precipitation changes are projected to increase at some stations and decrease at others, it is essential to note that different SSPs show different scenarios of future changes, which may change the general circulation patterns of the atmosphere in a way that increases precipitation at some stations and decreases at others under the influence of different precipitation systems, temperature, wind, topography, and vegetation. The difference in precipitation projection in different stations shows the complexity of climate changes and the influence of local factors on precipitation patterns.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Projection of summer precipitation in the near and middle future periods under SSP scenarios\u003c/h2\u003e\n \u003cp\u003eBefore examining the results, it is necessary to point out that the precipitation of this region occurs much less in the summer season than in other seasons, to the point where some studies avoid comparing the results of this season. It is necessary to consider this issue when using the results obtained. Considering this point, the examining of results of the changes in precipitation of the stations of the study area in the summer shows a decrease in the average precipitation values in the stations of Andorokh, Dowlatabad, Mashhad, Olang-e Asadi, and Sarasiab. However, in other stations, an increase in the average precipitation values in most periods of the near and middle future is evident. In general, the average precipitation of the stations showed that in the SSP1_2.6 scenarios, the near future (2025-54) and middle future (2055-84) periods increased by 3.05% and 16.59%, and in the SSP5_8.5 scenarios, the near future (2025-54) and middle future (2055-84) periods increased by 12.25% and 33.57%, respectively, compared to the same observation period (1991\u0026ndash;2020). The details illustrate in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. It should be noted that the number 33.57% corresponds to the average precipitation of the stations in the study area under the SSP5_8.5 scenario, the far future period, in the summer season, as the least rainy season in the study area. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows more details.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3. Projection of autumn precipitation in the near and middle future periods under SSP scenarios\u003c/h2\u003e\n \u003cp\u003eThe comparison of precipitation values in the base period and the future periods of the autumn season shows a decrease in most stations (Andorokh, Dawlatabad, Jaghargh, Mashhad, Radkan, and Sarassiab) in all scenarios. On average, in the study area in the autumn, under the SSP1_2.6 and SSP5_8.5 scenarios of the near future period, average precipitation decreases by 3.26% and 2.03%, respectively. Moreover, in the SSP1_2.6 and SSP5_8.5 scenarios, in the middle future, average precipitation was observed as 3.68 and 2.09%, respectively. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the projection of autumn precipitation in future.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Projection of winter precipitation in the near and middle future periods under SSP scenarios\u003c/h2\u003e\n \u003cp\u003eWinter is the rainiest season in the study area. According to the results, in almost all stations, the winter precipitation values show an increase in the middle future period under the SSP5_8.5 scenario than other scenarios. This issue was also evident in other seasons. Also, the lowest percentage of precipitation increase was observed in the near future period under the SSP1_2.6 scenario. Except for the SSP5_8.5 scenario of the middle future period, which shows an increase in precipitation in some stations, a significant decrease in rainfall was observed in almost all other scenarios in the winter season compared to the base period. In general, examining the average percentage of precipitation changes in stations shows an increase between 6.75% in the SSP1_2.6 scenario of the near future period to a rise of 20.96% in the SSP5_8.5 scenario in the middle future period. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the projection of autumn precipitation in future. The reason for this increase can be various mechanisms, including changes in pressure and temperature patterns, and as a result, changing path of precipitation systems and an increase in precipitation in the winter seasons in the study area.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5. Projection of annual precipitation in the near and middle future periods under SSP scenarios\u003c/h2\u003e\n \u003cp\u003eAs Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e depicts, Androkh, Dowlatabad, Jaghargh, Olang-e Asadi, Radkan, and Sarasiab stations show a decrease in precipitation in all scenarios of the future period compared to the base period. In other stations, a certain increase in the percentage of precipitation in each future period has been observed in different scenarios, the most significant increase being related to the pessimistic scenario of the middle future period. Also, in the SSP1_2.6 scenario, the near future period shows a smaller increase than the SSP5_8.5 scenario. In general, the mean annual precipitation of the stations in the study area shows a decrease of 2.92% and a rise of 3.44%, respectively, in the SSP1_2.6 and SSP5_8.5 scenarios of the near future period and an increase of 2.96 and 8.79%, in the SSP1_2.6 and SSP5_8.5 scenarios of the middle future period.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e4.6. Intercomparison of seasonal precipitation changes\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e shows the seasonal and annual precipitation changes in the study area, and Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the variability percentage of seasonal and annual precipitation. According to the available results, all the scenarios except the SSP5-8.5 scenario show a decrease in precipitation in the spring season. The summer, the least rainy season in the study area, showed almost the same results as the base period with a slight increase. In the autumn, all scenarios show a slight decrease compared to the base period. In the winter, all scenarios show an increase in precipitation, which is a higher percentage in the middle future, SSP5_8.5 scenario. The lowest increase in the winter season is related to the near future period under SSP1_2.6 scenario.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe results of the CMhyd model evaluation based on R2, RMSE, and KGE criteria showed that in the study area, the simulation was performed more accurately by the MIROC6 model. The mean of KGE and RMSE coefficients for precipitation were 0.91 and 21.09, respectively. The mean annual rainfall under the SSP1_2.6 and SSP5_8.5 scenarios in the near future (2025\u0026ndash;2054) decreased by -2.92 and increased by 3.44 percent, respectively, and under the SSP1_2.6 and SSP5_8.5 scenarios in the middle future (2055\u0026ndash;2084) increased by 2.96% and 8.79%, respectively, compared to the observation period. In terms of seasonal comparison, in most scenarios of future periods, a decrease in precipitation between 1.51% and 8.18% was observed in the spring and autumn seasons, and only in the SSP5_8.5 scenario of the distant future period (2055-84), there was an increase in precipitation in the spring season by 3.56%. In the summer and winter seasons, in all scenarios, an increase in rainfall between 3.05 and 33.57% was observed.\u003c/p\u003e \u003cp\u003eAccording to the available results, all scenarios except the SSP5_8.5 scenario show a decrease in precipitation in the spring season in the middle future. The summer season, which is the least rainy season in the study area, showed a slight increase in the precipitation for future periods. In the autumn, all scenarios show a slight decrease compared to the base period. In the winter season, it shows an increase in precipitation in all scenarios, which in the SSP5_8.5 scenario has a higher percentage, which may be due to the rise in the intensity of extreme precipitation events that can lead to flooding and damage. These changes reflect the complex nature of climate change and its varying impacts on different regions. A comparison of various studies conducted based on different models of the IPCC Sixth Assessment Report (Afsari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Babaeian et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; ShiftehSome\u0026rsquo;e et al., 2012; Zenozi Alamdari et al., 2025) showed that the results of some of the studies conducted are consistent with the results of this study.\u003c/p\u003e \u003cp\u003eThe results of this research can be used as a management tool for planning and decision markers to deal with the impacts of climate change on the water resources of the Kashafrud catchment, which is necessary to determine the impact of increasing or decreasing precipitation on other hydrological parameters such as runoff, evaporation, and soil permeability should also be checked. Increasing precipitation in the winter season can help improve water resources, but on the other hand, increasing heavy precipitation events and changing seasonal patterns create a new challenge for water resources management. Therefore, in addition to adapting to these changes, investing in catchment infrastructure, improving irrigation systems, and increasing the efficiency of water consumption is necessary to ensure water security in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eConflict of Interest:\u003c/strong\u003e \u003cp\u003eThe authors have no conflicts of interest to declare that they are relevant to the content of this article.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eEthics approval\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate:\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eAll Authors consent to the article's publication after acceptance.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding Statement:\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organisation for the submitted work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study's conception and design. Hossain Imanipour and Mokhtar Karami performed material preparation, data collection, and analysis carried out by Mokhtar Karami and Abdolreza Kashki. Hossain Imanipour and Morteza Esmailnejad wrote the first draft of the manuscript and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAfsari, R., Nazari-Sharabian, M., Hosseini, A. \u0026amp; Karakouzian, M. 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Stud.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e (4), 9761\u0026ndash;9753 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 3 to 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, CMIP6, Rainfall, Kashafrud catchment","lastPublishedDoi":"10.21203/rs.3.rs-5953463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5953463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is known as one of the most critical challenges facing humanity. These changes seriously threaten the Kashafrud River, a significant water resource in northeastern Iran. This study aims to project the impact of climate change on precipitation in the Kashfrud catchment in Iran. The output data of global climate models, including ACCESS-ESM1-5, MRI-ESM2-0, and MIROC6 from the sixth-generation models (CMIP6), have been used to project future climate changes. Based on the accuracy of statistical criteria, the linear scaling (LS) method was employed to do the bias correction of precipitation data. Then, using the CMhyd model, the precipitation data were simulated for the two future periods (2025-54) and (2055-84) under the SSP1_2.6 and SSP5_8.5 scenarios. The average KGE and RMSE coefficients for the precipitation of the selected MIROC6 model were obtained as 0.91 and 21.09, respectively. Average annual rainfall under the scenarios of SSP1_2.6 and SSP5_8.5 in the near future period decreased by 2.92% and increased by 3.44%, respectively. Average annual rainfall was projected as follows: under the SSP1_2.6 and SSP5_8.5 scenarios in the near future period, it decreased by 2.96% and increased by%3.44, and under the SSP1_2.6 و SSP5_8.5 scenarios in the middle future period, it increased by 2.96% and 8.79%, respectively compared to the observation period (1991\u0026ndash;2020). In terms of seasonal comparison, in most of the scenarios of the future periods, in the spring and autumn seasons, precipitation will decrease between 1.51% and 8.18%. Precipitation in the future seasons in summer and winter was projected with a slight increase. The results showed that climate change will have a significant impact on the precipitation of the study area. This can have severe consequences for different sectors, including agriculture, industry, and the environment. The research results can be employed as a management tool in the direction of water resources management.\u003c/p\u003e","manuscriptTitle":"Projecting the impacts of future climate change on precipitation in the Kashafrud catchment in Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-11 09:54:49","doi":"10.21203/rs.3.rs-5953463/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aca39718-0eec-4f98-80ea-b97e134ed9f0","owner":[],"postedDate":"February 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44056602,"name":"Earth and environmental sciences/Climate sciences/Climate change"},{"id":44056603,"name":"Earth and environmental sciences/Climate sciences"},{"id":44056604,"name":"Earth and environmental sciences/Environmental sciences"},{"id":44056605,"name":"Earth and environmental sciences/Hydrology"}],"tags":[],"updatedAt":"2025-03-06T11:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-11 09:54:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5953463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5953463","identity":"rs-5953463","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}