How is climate change in Iran?

Iran's climate is classified by the De Martonne aridity index, and then the changes in Iran's climate classes under the effects of climate change in the future periods, according to the output of the CanESM2 model from the CMIP5 modes, which is downscaled using the LARS-WG model. It has been investigated according to two emission scenarios, RCP2.6 and RCP8.5. The results indicated that the arid climate with 68.82% and the semi-arid climate with 21.97% constitute the largest area of Iran. The remaining climatic classes collectively comprise less than 10% of Iran's area. Therefore, Iran should be called an arid and semi-arid country in terms of climate. Investigating the effects of climate change on precipitation and temperature showed that both precipitation and average temperature will increase in future periods. However, the increase in both variables will be greater under the RCP8.5 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The findings of this study indicate the necessity of addressing the issue of climate change and the importance of involving experts and macro planners in the analysis of the effects of climate change. It is suggested to use the output of other GCM models in future research due to the uncertainty of climate scenarios. Also, the use of diverse climate classification methods that incorporate other variables is suggested for more precise identification of climate characteristics .According to the results, the majority of Iran (90.49%) has an arid and semi-arid climate. The percentage of arid climate is 68.82%, while that of semi-arid climate is 21.97%. Therefore, Iran should be called an arid and semi-arid country in terms of climate. By analysis of the effects of climate change indicates that in future periods, the precipitation and average temperature will increase. This increase will be greater under the RCP8.5 scenario than the RCP2.6 scenario. The study of the climatic classification of Iran in the coming periods indicates that the majority of the country will continue to experience arid and semi-arid climates. The sum of arid and semi-arid climates will reach its lowest level in the period of 2020-2041. This is following the RCP2.6 scenario, after which these climates are expected to expand once more. According to the RCP8.5 scenario, during the periods of 2021-2040, 2041-2060, and 2061-2080, the total area of arid and semi-arid climates will decrease. However, from 2081 to 2100, this trend will be reversed, increasing in these climates. According to the results of this research and according to the forecast, although according to different release scenarios, the difference in the area of different classes can be seen, in the future, arid and semi-arid climatic zones will still form the majority of Iran.The average weather condition in a specific region is defined as climate. The diversity of climatic variables is effective in determining the climate of a region and causes the formation of diverse and different climates. One of the effects of climate change is that causes an increase or decrease in a climate zone and, as a result, a shift in climate zones. Climate classification is an attempt to identify and recognize the differences and similarities of climate in different regions and to discover the relationships between different components of the climate system. Climate classification indicators are used to visualize current climate and quantify future changes in climate types as predicted by climate models. The studies conducted on these methods show that climatic variables affecting experimental methods such as temperature and precipitation should be considered effective variables in determining climatic boundaries in a new way. The De Martonne aridity index is an empirical index for climate classification based on two components, precipitation and temperature. Due to its high accuracy, and the use of variables that are more accessible and can be measured at most meteorological stations, De Martonne’s index has received more attention from researchers and has been used in many studies of climate change. Therefore, the purpose of this research is to evaluate the effects of climate change on the climatic classification of Iran.Material and methods Season defnition The method of local temperature threshold is used to determine the onset and end of the four seasons using daily temperature data. The local temperature threshold procedure has frequently been used in the literature to determine seasonal cycles and lengths (Christidis et al. 2007; Park et al. 2018; Wang et al. 2021). First, a fourthdegree polynomial equation is ftted through daily temperature data at each station to smooth the variability of daily temperatures. Then, temperature thresholds for 25th and 75th percentiles are determined using smoothed daily temperature data. Now four seasons can be determined as the following: (a) Summer onsets when the temperature exceeds the 75th percentile and ends when the temperature falls below the 75th percentile. Thus, summer is the warmest quarter of the year. (b) Winter onsets when the temperature falls below the 25th temperature percentile and ends when the temperature exceeds this threshold value. As a result, winter is the coldest quarter of the year. (c) Spring which is a transitional season between winter and summer characterized by increasing temperature (d) Autumn, a transitional season from summer to winter, characterized by decreasing temperature. For each season, its length is equal to the diference between its onset and end. The defnition of four seasons is presented schematically in Fig. 2. Therefore, lengths of four seasons at each station are evaluated for the observation period (1980–2014). Knowledge of the contribution of seasonal precipitation to the annual cycle is a critical issue for water resources planning and management. The rainfall contribution of a given season to annual precipitation represents the percentage of annual rainfall that occurs in a specifed season. in which Cs is the rainfall contribution of a season to annual rainfall,Ps is the precipitation depth during the specifed season (mm), and P is the annual precipitation depth (mm).Statistical transient downscaling Here, downscaled daily precipitation and temperature data based on outputs from the multi-model ensemble of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) is used to study seasonal characteristics within Iran under climate change (Naderi et al. 2024). However, details about their methodology and uncertainty analysis are available in the literature (Naderi et al. 2024), and a brief description of downscaling methodology is presented here. Statistical downscaling serves as an efcient, cost-efective method for translating general circulation model (GCM) outputs from a coarse horizontal resolution to a fner resolution (Wilby et al. 2004). The statistical downscaling using the Long Ashton Research Station weather generator (LARS-WG) provides downscaled GCMs output at the fnest spatial resolutions, i.e., at the scale of a point or single station (Semenov 2007; Semenov and Barrow 2002). The standard procedure for constructing daily weather data for a climate change scenario involves three steps. The frst step (calibration) computes parameters defning the empirical statistical distributions of temperature and precipitation for each month of the baseline period using long-term weather data observations. The second step (validation) assesses the performance of LARS-WG by comparing generated and observed monthly data using statistical tests. The fnal step (prediction) involves using the calculated change factors from the GCM data to generate daily data at each station under a specifc scenario (Semenov 2007; Semenov and Barrow 2002). The LARS-WG was calibrated for each station using observed daily temperature (minimum and maximum) and precipitation data over a 35-year period (1980–2014). During the calibration process, LARS-WG calculates the probability distributions of temperature, precipitation, and the length of wet and dry spells for each month. It then fts semi-empirical exponential functions to these data, allowing it fexibility, and facilitates the modeling of a wide variety of distributions (Semenov 2007). The performance of the LARS-WG at each station was evaluated by generating 35-year daily temperature and precipitation data and comparing these data to the observed values. The LARS-WG performance was verifed by comparing the seasonal distributions of wet and dry series, as well as the monthly distributions of temperature and precipitation, with observed values using the Kolmogorov–Smirnov (K-S) test. The monthly means of temperature and precipitation were also compared using Student’s t-test (Iizumi et al. 2012; Semenov and Stratonovitch 2015). The signifcance level was set to be 0.01 for all tests. The results of the K-S tests and t-tests at all stations indicated that the LARS-WG performs well in reproducing temperature and precipitation data (Naderi et al. 2024). To downscale the GCM data under a specifc SSP for a given station, the change factors for the relevant GCM grid box must be applied. The LARS-WG uses a monthly perturbation method, which modifes statistical distribution parameters by change factors, to downscale future daily data under the infuence of GCM output (Semenov and Barrow 2002). The change factors (CFs) for the standard deviation of temperature, precipitation depth, mean length of wet spell, and mean length of dry spell for each calendar month i are calculated as the ratio of corresponding values during the future period to those of the observation (baseline) period (Semenov and Barrow 2002).Discussion This study uses downscaled daily precipitation and temperature data with minimal uncertainties based on output from the multi-model ensemble of the CMIP6 under three new climate change scenarios (SSP1-1.9, SSP2-4.5, and SSP5- 8.5) to evaluate the impact of climate change on seasonal characteristics at 51 stations across Iran. The study fnds that global warming will change season length and the seasonal contribution of rainfall to annual cycles in the future, which is crucial information for sustainable activities, energy use, scientific research, and policy-making in Iran. Climate change will lead to the shortening of spring and autumn and the lengthening of summer and winter seasons. However, the extent of these length anomalies depends on the region and the climate change scenario. Meanwhile, the warming level is greater in summer and less in winter. Change in seasonal precipitation strongly depends on the season and climate change scenario, with a wide spatial variation across the country. Climate change may shift the wet season from winter to spring or autumn, depending on the location and scenario.Changes in season length and shifts in the wet season will profoundly afect various sectors such as energy, agriculture, economy, social welfare, and ecosystems in Iran. Alterations in seasonal characteristics can substantially impact water availability and quality, water resources management strategies, evapotranspiration, soil moisture content, timing and peak fow of fooding, water levels in rivers and reservoirs, groundwater recharge rate and timing, groundwater levels in aquifers, and ultimately water allocation and use (Ayejoto et al. 2023; Egbinola and Amanambu 2014; Larbi et al. 2021). Increased irrigation demands and competition for limited water resources can lead to further over-exploitation of groundwater in Iran. This study highlights that current water management strategies may fail in a warming future, serving as an alarm for water managers and policy-makers. Changes in season length can lead to shifts in plant phenology, such as earlier blooming and longer periods of photosynthesis, which can afect the overall productivity and carbon balance of ecosystems. This can have cascading efects on species that rely on specifc plants for food or habitat, potentially leading to mismatches in ecological relationships (Scholz et al. 2018). Shifts in wet seasons cause shifts in available water, which in turn lead to mismatches in the life cycles of fora and fauna. The extinction of very sensitive species that cannot rapidly adapt may occur during future warming decades. Changes in seasonal characteristics will afect crop yields, planting schedules, pest management, and especially irrigation timing and demand (Barati et al. 2024; Faradila and Bowo 2023; Shahpari et al. 2023). Seasonal changes in precipitation and temperature patterns can afect soil moisture content and crop water requirements, complicating irrigation needs and schedules for irrigated crops (Bedane et al. 2022). Irrigation management (schedule and timing) may become a critical challenge for farmers in the future due to a lack of experience under new conditions, potentially leading to loss of crop productivity and farmer income. Other challenges may include the need to shift cropping patterns to compensate for changes in seasonal characteristics, especially for rain-fed crops under a warmer climate. These challenges necessitate adaptive agricultural practices to sustain productivity, which is crucial for food security and livelihoods in the face of changing seasonal dynamics. Seasonal variations afect diferent sectors such as the economy, energy, and society. In the energy sector, shifts in season length can alter heating and cooling demands, infuencing energy consumption patterns and costs. Longer summers may increase the demand for cooling, while extended winters may drive higher heating needs. Additionally, tourism-dependent economies can experience fuctuations in visitor numbers and revenue due to changing peak seasons, impacting employment and local businesses. These interconnected impacts highlight the necessity for integrated management approaches to adapt to and mitigate the efects of changing seasonal patterns in Iran. This study assesses the impact of climate change on future seasonal characteristics in Iran using statistical downscaling. While statistical downscaling provides reliable data for further impact assessments of climate change across various felds, it does not ofer insights into the physical causes and mechanisms behind the inferred results. Conversely, dynamic downscaling can provide valuable information to understand the causes and mechanisms responsible for changes in seasonal patterns. However, dynamic downscaling may be limited to regions with sufcient input data and budget. Consequently, the results of this study ofer valuable information for sustainable development and can be used for further impact assessments of climate change across diferent felds. However, dynamic downscaling is highly recommended for Iran. This study evaluates season length and seasonal rainfall and temperature patterns under climate change, but future work may focus on further analyses, such as seasonal potential evapotranspiration, actual evapotranspiration, and irrigation water requirements under global warming which is another critical issue for sustainable development in water resources and food production. It is highly recommended to compare the performance of transient statistical downscaling used in this study with other robust methods, such as machine-learning techniques, to evaluate the impact of climate change on seasonal characteristics in Iran.Conclusions This study finds that Iran has experienced summer (~97 days) by 10 days longer than winter (~ 87 days) while spring and autumn lengths over the country are about 90 days during the baseline period (1980–2014). Meanwhile, climate change will lead to lengthening summer and winter seasons (up to 15%) and shortening spring and autumn seasons (up to 10%) in the future. Seasonal temperature analysis during the baseline period indicates that the temperature of spring and autumn over Iran is about 17.5 ◦C, summer temperature is about 28.0 ◦C, and winter temperature is 7.0 ◦C. Furthermore, temperature diference among stations is large (17.5–22.1 ◦C) during a given season, implying a wide range of spatial variations of temperature across the country. The southeastern and northwestern Iran experience the warmest and coldest seasons, respectively. Global warming, however, will increase the temperature of all seasons of spring and autumn (1.5–2.7 ◦C), winter (1.1–2.2 ◦C), and summer (2.1–3.2 ◦C) during the future period (2021–2080) of three SSPs in which temperature enhancement is minimum under SSP1-1.9 and maximum under SSP5-8.5. Minimum and maximum warming will occur during winter and summer,respectively, while moderate temperature enhancement will occur during spring and autumn seasons under a given SSP. Seasonal precipitation analysis during the baseline period reveals that all stations within the country (except in northern Iran) have experienced precipitation during spring, autumn, and winter seasons where contributions of winter and spring rainfall are predominant throughout the year. The northern regions have experienced precipitation during all seasons where the contribution of autumn rainfall is predominant throughout the year. Countrywide, contributions of spring, summer, autumn, and winter seasons to the annual rainfall are 32%, 5%, 20%, and 43%, respectively, implying winter and spring as wet seasons during the baseline period. The majority of regions tend to experience increased spring and winter rainfall but reduced autumn rainfall under SSP1-1.9; however, rainfall will increase during all seasons, depending on the season, by about 6–36% on the national scale. Warmer global warming scenarios will lead to a decrease in seasonal rainfall by about 12–24% and 8–24% under SSP2-4.5 and SSP5-8.5, respectively. Contributions of spring and winter will increase but autumn contribution will decrease under SSP1-1.9, leading to a shift in the wet season from winter to spring in the majority of regions. The wet season over most stations may shift from winter to spring or autumn under warmer SSPs, depending on the station and SSP. However, the winter contribution over Iran does not change signifcantly, the spring contribution will reduce (2–3%), and the autumn contribution will slightly increase (1.5%) under warmer SSPs. Adopted strategies for water resources planning and management, food production, and energy consumption strongly depend on seasonal characteristics of precipitation and temperature. The duration of wet and dry seasons, associated with rainfall onset, withdrawal, and distribution throughout the year, afects water abstraction and agricultural production. Crop’s water demand over Iran will increase under a future warmer climate due to increased temperature, highlighting a large increase in water demand during longer and warmer summers in the future. Hence, water abstraction may further increase in the future to compensate for climateinduced water shortage, leading to an intensifed water crisis. The water crisis may further be intensifed due to decreased seasonal rainfalls under moderate to high levels of global warming. Shifting the wet season from winter to spring or autumn will substantially afect the seasonal surface water and groundwater availability, groundwater recharge, food timing, seasonal water abstraction, dam operation, and therefore the water cycle in the future. It is crucial to adapt and develop strategies considering the aforementioned issues to ensure sustainable water management and food production over Iran. Scientifc and technological support from researchers and policymakers is necessary to overcome these challenges.