Introduction

Nowadays, due to the severe impacts of climate change and global warming on the structure and function of various ecosystems and production systems, the most concerning disruptions caused by human activities are for modern society . Natural and human activities lead to the warming of the Earth's surface and, consequently, an increase in greenhouse gas concentrations. However, climate change has been exacerbated since the 1970s, particularly due to fossil fuel consumption, population growth, industrialization, deforestation, extensive agriculture, and land-use changes by humans . The direct impacts of climate change include numerous phenomena. One consequence of climate change is rising temperatures and precipitation variability, which are accompanied by an increase in extreme weather events such as droughts, floods, hailstorms, heatwaves, sea-level rise, cold waves, and wildfires. In recent decades, General Circulation Models (GCMs) have been used to predict climate change impacts on various systems, including water resources and more.

Therefore, this research investigates the effects of climate change on temperature and precipitation parameters using CMIP6 climate models in Poldokhtar County, located in Lorestan Province. The study utilizes data from the CanESM5 predictor model and the Lars-WG downscaling model, based on emission scenarios developed for the baseline period (1992-2022), to forecast future atmospheric conditions. Two scenarios - the optimistic SSP1-2.6 and pessimistic SSP5-8.5 - will be examined for a near-future twenty-year period spanning 2023 to 2043.



Materials and methods

First, meteorological data for Poldokhtar County were obtained from the Lorestan Province Meteorological Organization. Then, by collecting observational time series data of daily temperature and precipitation over a 30-year baseline period (1992–2023), their characteristics and trends were examined, and statistical tests were performed on the two parameters (temperature and precipitation) to analyze them. Next, by referring to available databases related to the Sixth Assessment Report (AR6), a General Circulation Model (GCM) was selected, and time series data generated by different models were retrieved. The projected climate data from these models for Lorestan Province were accessible based on the specified longitude and latitude range. To achieve the objectives of this study, historical climate data from the CanESM2.0 model in NetCDF format (daily and monthly scales) were downloaded from climate data portals. For statistical processing, the data were converted to Excel format using ArcMap software. The performance capability of the selected model in simulating temperature and precipitation parameters during the baseline period was evaluated using the model's historical data. In the next stage, the required future climate data were statistically downscaled and projected using the LARS-WG software environment for the CanESM5.0 model, based on the IPCC Sixth Assessment Report (AR6), for two future 20-year periods (2020–2050).

Results and Discussion

Results indicated that both examined GCM models project an increase in minimum and maximum temperatures during the future period (2020–2049) compared to the baseline (1990–2019). According to the SSP results, both maximum and minimum temperatures increased under both the pessimistic (high-emission SSP5-8.5) and optimistic (SSP1-2.6) scenarios, with nearly identical magnitudes. However, precipitation declined more sharply under the pessimistic scenario (SSP5-8.5) compared to the low-emission (optimistic) scenario. Overall, the SSP5-8.5 scenario (higher emissions) led to higher projected temperatures than the SSP1-2.6 scenario (lower emissions), with this difference being particularly pronounced during warmer months. The models also exhibited divergence in precipitation projections, though a general declining trend in rainfall under the high-emission scenario (SSP5-8.5) was consistent across both models. This reduction in precipitation was especially evident during summer months, suggesting an elevated risk of drought in the region.

Conclusion

The model performance evaluation results demonstrated that CanESM5.0 can accurately simulate maximum and minimum temperature parameters, but shows greater error in simulating precipitation compared to the other two parameters. The temperature change results revealed that the studied county is affected by global warming during the examined period, with temperature changes indicating increases during 2020-2050 under both SSP126 and SSP585 scenarios. The precipitation and temperature variability prediction results showed that the BCC-CSM2-MR model predicts significantly higher precipitation from June to October, while the SSP585 scenario generally results in higher temperatures compared to SSP126. Furthermore, the results indicated temperature increases across all examined scenarios, particularly in higher emission scenarios (SSP585), along with significant differences in precipitation predictions among models. These reports serve as reliable resources for organizations, policymakers, and the scientific community to make appropriate decisions regarding climate change impact management and mitigation.