Understanding and predicting regional climatic variations is crucial for agricultural planning, water resource management, and disaster mitigation in the face of increasing global climate instability. This study presents a comprehensive stochastic simulation framework designed to model the diverse meteorological profiles of Türkiye’s seven geographical regions. The developed model utilizes a randomized structure that learns monthly parameters from historical daily datasets spanning the 2010–2024 period and generates daily temperature and precipitation scenarios for 2025. Methodologically, a Normal Distribution was employed for temperature modeling, while a first-order Markov Chain was utilized to determine the occurrence of precipitation. To account for the characteristically positive and right-skewed nature of rainfall intensity on wet days, the Gamma Distribution was preferred a standard approach in current literature for modeling daily precipitation amounts. To capture the inherent stochasticity of atmospheric processes, a Monte Carlo simulation approach was implemented. Each representative city was initially simulated with 300 iterations to ensure statistical robustness. To rigorously assess predictive accuracy, validation for 2024 was performed using a Monte Carlo simulation with 400 independent runs. The findings demonstrate that the model effectively captures regional climatic trends and provides a reliable synthetic dataset for environmental planning. Validation metrics indicate strong agreement between modeled and observed climatic behavior, with historical monthly temperatures, precipitation totals, and wet-day probabilities consistently falling within the simulated 95% confidence intervals. These results suggest that integrated stochastic models offer a high-fidelity and computationally efficient alternative to complex numerical weather predictors for regional climate assessment.
Kılıç et al. (Tue,) studied this question.