Extended Abstract
Introduction
Precipitation is a critical component of the hydrological cycle, significantly influencing water resource management across diverse regions. To optimize water utilization within basins, a comprehensive examination of precipitation patterns is essential. Global warming has induced climate change, affecting various aspects of human life and the environment. These alterations directly modify precipitation amounts and evapotranspiration rates while indirectly influencing the hydrological cycle through the occurrence of extreme weather events. Changes in precipitation patterns and water distribution can have profound consequences for agriculture, potable water supplies, and ecosystems. Therefore, understanding and rigorously analyzing these processes and their implications are crucial for the planning and sustainable management of water resources. Climate change represents one of the most pressing global challenges of recent centuries, resulting in severe and unforeseen phenomena that have significant implications for human societies and ecosystems. The consequential impacts include rising temperatures, altered precipitation patterns, and an increased frequency of natural events such as storms and floods. Addressing this crisis necessitates international collaboration and the development of sustainable solutions to mitigate its adverse effects and ensure a better future for generations to come.
 
Methodology
This research investigates climate change and its ramifications across various sectors of East Azerbaijan Province. To achieve this, meteorological data and generative models are employed. The LARS-WG model is selected as a tool for assessing self-capabilities. For this analysis, observational data spanning 20 years from six synoptic stations within East Azerbaijan Province, collected from 1995 to 2014, was utilized. In order to project future conditions, data for the period 2021-2040 was generated using the CMIP6 model under three distinct scenarios: the optimistic scenario (SSP1-2-6), the intermediate scenario (SSP2-4-5), and the pessimistic scenario (SSP5-8-5). The stations considered in this study include Tabriz, Maragheh, Ahar, Mianeh, Jolfa, and Sarab. Daily precipitation data from the six synoptic stations for the period 1995 to 2014 was examined to compute extreme precipitation indices. These indices, which are designed to evaluate weather patterns and climate variability, conform to the global standards established by the Expert Team on Climate Change Detection and Indices (ETCCDI). The goal of analyzing this data is to enhance the understanding of precipitation patterns and their impact on regional weather conditions. The assessment of extreme indices may facilitate more accurate predictions and improved planning in response to climate change, ultimately contributing to a deeper understanding of climate change and its effects on various regions.
 
Results and Discussion
Simulated results were compared with observed data to evaluate the model's capacity to replicate precipitation patterns. Future precipitation was subsequently simulated for the period 2021-2040 under three distinct climate change scenarios. At the studied stations, the highest levels of precipitation were both observed and simulated in April and May, whereas the lowest levels occurred in August. Although the LARS-WG7 model demonstrated commendable performance in simulating general precipitation patterns, discrepancies were noted in the amounts and timing of precipitation between the observed and simulated datasets. Overall, peak precipitation in East Azerbaijan was associated with April and May, while minimum levels were recorded in August. Results from the R10mm index in the ACCESS-ESM1-5 model for the SSP1-2-6, SSP2-4-5, and SSP5-8-5 scenarios during the 2021-2040 period indicate that precipitation exceeding 10 mm is projected to increase in the mountainous and northern regions
 
of East Azerbaijan, while a decrease is anticipated in the eastern regions. The Ahar and Tabriz stations exhibited the highest frequency of extreme precipitation events. Furthermore, the R95P index suggests an increasing frequency of such events in the southwest and southeast, while a decline is observed in the northwest. The Maragheh, Sarab, and Mianeh stations recorded the highest frequency for the R99P index, whereas Jolfa exhibited the lowest frequency. The results of the trend and p-value analysis of the R10mm, R95p, and R99p indices for the SSP1-2-6, SSP2-4-5, and SSP5-8-5 scenarios in East Azerbaijan from 2040 to 2021, compared to observed data from 1995 to 2014, indicate a decreasing trend in heavy precipitation. The Tabriz, Ahar, and Jolfa stations displayed the most significant trends, while Maragheh and Sarab showed the least. P-values exceeding 0.05 at certain stations suggest insignificant changes. Moreover, these analyses underscore the necessity for further investigations to accurately comprehend variations related to climate change.
 
Conclusion
The findings indicate that the intensity and frequency of extreme precipitation events are projected to increase significantly in certain regions while decreasing in others. These changes pose severe risks, including an increased likelihood of flooding, soil erosion, infrastructure damage, and detrimental effects on agriculture and water resources. This underscores the necessity for policymakers and planners to integrate these findings into future water resource management and disaster risk reduction strategies. By doing so, they can effectively mitigate the impacts of climate change and adapt to the evolving climatic conditions that threaten the region's sustainability and resilience. Addressing these challenges will be crucial for ensuring a stable future for communities affected by climate variability.