Introduction: Rising air temperatures and erratic rainfall distribution patterns pose challenges to water access and crop productivity in the 21st century (Shayanmehr et al., 2022; Yang et al., 2020; Zhang et al., 2021). Climate change, characterized by rising air temperatures, is one of the consequences of global warming (Mo et al., 2017) and is likely to pose serious challenges to ecosystems, economic conditions, and people's quality of life in the 21st century (Liu, 2022; Sha et al., 2019). One of the main objectives of this study is to assess the increasing or decreasing trend of monthly precipitation and temperature using conventional Mann-Kendall trend analysis. Secondly, the prediction of precipitation and air temperature changes in the Jiroft Plain using five GCM models (GFDL-ESM4, IPSL-CMA6-LR, ACCESS-CM2, MIROC6 and NORESM2SM2-MM) under newly developed SSPs (ssp1-2.6, ssp2-4.5 and ssp5-8.5) in the near future (2025 to 2054) and far future (2055 to 2084) periods using the well-known statistical downscaling model (LARS-WG). This study is of great importance to water resource planners and agricultural farm managers, as it provides valuable insights into the projected changes and variability of climate variables under future climate scenarios. This is particularly relevant for the vulnerable Jiroft Plain region, which has the potential to impact national food security.
Methodology: The meteorological station of the study area is located in the southeastern part of Kerman province, in Jiroft county, as shown in (Figure 1). The Jiroft plain is part of the western Jazmorian basin, which is located between longitudes 15'57' and 17'58' East and latitudes 12'28' and 13'29' North, in the south of Kerman province in southeastern Iran. Meteorological data for the base years (1993-2022), including precipitation, solar radiation, maximum and minimum air temperatures, required for trend analysis and implementation of the Lang Ashton Research Station (LARS-WG) climate model, were collected from the Jiroft Meteorological Department. After analyzing the baseline trends of precipitation and temperature, the next step involves predicting future changes in climate elements under different climate scenarios using global circulation models (GCMs). Calibration, validation and acquisition of future climate data were the steps to generate simulated precipitation and temperature data for both current and future climates. To establish model parameters and generate simulated daily climate data, the LARS-WG model was calibrated based on 30-year baseline data (from 1993 to 2022) at the study station. In the present study, five GCM models included in the LARS-WG8 model, under three SSPs, as presented in Tables (1) and (2), respectively, were used to predict changes in precipitation and minimum and maximum temperatures. The results were examined both as an ensemble of mean GCMs and individually to evaluate the prediction of future climate variables.
Results and Discussion: Based on the tau statistics and p values, MK test, the annual trend of the available precipitation shows that a significant decreasing trend has been observed using the Mann-Kendall test calculation at 95% levels, which is consistent with the results of (Elham Rafiei and Ali Azare, 2019), which stated that the precipitation values have a significant decrease. The examination of the annual trend of the available temperature shows that there is a significant increasing trend at 95% levels using the Mann-Kendall test calculation. Which is consistent with the results of (Elham Rafiei and Ali Azare, 2019), which stated that the temperature values have a significant increase. High R2 values and low RMSE values for the LARS-WG model showed that the model has the capacity to downscale the climate variables in current conditions and future climate scenarios. The results obtained were in line with the report of Afsharipour et al. (1402) and Barkhouri et al. (1399) in studying the climate changes of Jiroft. The results of precipitation modeling according to Table (5) showed more fluctuations in the distant future compared to the base period. Overall, according to the results of different scenarios, it seems that the distant future period is the rainiest period based on the SSP1-2.6 and SSP5-8.5 scenarios. However, it is the rainiest period under the SSP2-4.5 scenario. Therefore, compared to the observed period, all three scenarios show an increase in temperature during the study period. SSP5-8.5 shows a greater increase than SSP1-2.6 and SSP2-4.5, which is due to increased GHGs. It is acknowledged that climate change changes the timing and duration of the season, which may not be fully reflected by conventional static models. In the wake of these ongoing changes, it would be wise for future research to investigate how to incorporate dynamic, climate-based definitions of seasons, which would allow for a more accurate assessment of the impacts of climate change. Thus, more accurate predictions of the impacts of seasonal change on ecosystems, agricultural practices, and human activities could be made in future studies, leading to better adaptation tactics.
Conclusion: The results showed that the Mann-Kendall method provides a significant trend in the climatic parameters of temperature and precipitation of the meteorological station. The results also show that the winter and spring seasons have the largest contribution to the changes in the average monthly precipitation and the summer and autumn seasons have the largest contribution to the changes in the average temperature. The feasibility of the LARS-WG8 model for downscaling climate variables, including Pr, Tmin and Tmax, in the Jiroft Plain region has been demonstrated. In general, more precipitation is predicted in the second period compared to the first period under the ssp5-8.5 and ssp1-2.6 scenarios. Tmean is predicted to experience a gradual increase in the two near- and far-future time periods in the three SSPs (ssp1-2.6, ssp2-4.5 and ssp5-8.5) for all five models. The largest increase in Tmean is predicted in the second long-term period compared to the first period.