Crop simulation models are advanced tools that integrate agronomic, environmental, and climatic data to simulate the growth, development, and yield of crops under varying conditions. These models are designed to predict how crops respond to environmental factors such as temperature, rainfall, soil properties, nutrient availability, and management practices. In the context of climate change, crop simulation models become particularly valuable because they allow researchers, policymakers, and farmers to anticipate potential impacts of changing climatic patterns on agricultural productivity and to design strategies for adaptation and mitigation. One of the key features of crop simulation models is their ability to represent the physiological processes of plants. These processes include photosynthesis, respiration, phenological development, transpiration, and partitioning of biomass to different plant organs. By quantifying these processes, models can predict the growth trajectory of a crop from planting to harvest. For example, models such as DSSAT (Decision Support System for Agrotechnology Transfer), APSIM (Agricultural Production Systems sIMulator), and AquaCrop are widely used to simulate crop performance under different environmental scenarios. These models consider factors like water use efficiency, nutrient uptake, and the impact of pests and diseases on crop growth, allowing for a comprehensive understanding of crop-environment interactions. Another important feature is the capacity of crop simulation models to incorporate climatic variability. Climate change introduces uncertainties such as erratic rainfall patterns, increased frequency of droughts and floods, and rising temperatures. Crop models can integrate historical and projected climate data to estimate how these factors influence crop phenology and yield. For instance, using temperature projections from global climate models (GCMs), crop simulation models can forecast the potential shifts in planting dates, crop duration, and yield levels. This is particularly important for regions prone to extreme weather events, as it enables proactive planning for risk management and resource allocation. Crop simulation models are also highly flexible in their capacity to test different management scenarios. By altering variables such as planting date, irrigation schedules, fertilizer application rates, and crop varieties, these models can identify strategies that maximize yield while minimizing negative environmental impacts. In the context of climate change assessment, this capability is essential for exploring adaptive measures. For example, in semi-arid regions, models can help determine the most suitable crop varieties and irrigation strategies to cope with increased heat stress and water scarcity. Similarly, in areas experiencing increased rainfall variability, models can suggest optimal planting times and soil management practices to reduce the risk of crop failure. The spatial and temporal resolution of crop simulation models is another notable feature. Some models operate at the field scale, providing detailed insights