Crop simulation models have emerged as essential tools for assessing the potential impacts of climate change on agricultural systems, providing valuable insights for researchers, policymakers, and farmers. These models are designed to mimic the growth and development of crops under varying environmental conditions by integrating knowledge of plant physiology, soil processes, climate variables, and management practices. Their ability to capture the complex interactions among these factors makes them powerful instruments for understanding how crops respond to changes in temperature, precipitation patterns, carbon dioxide concentrations, and extreme weather events. By evaluating different scenarios, crop simulation models contribute to more informed decision-making in the face of growing climate uncertainty. A central feature of crop simulation models is their reliance on process-based frameworks. Unlike purely statistical models, which depend on historical data to predict outcomes, process-based models simulate the underlying biological and physical mechanisms that govern crop growth. This includes photosynthesis, respiration, transpiration, nutrient uptake, and biomass partitioning. Such models are structured to represent the life cycle of a crop, beginning with germination and proceeding through vegetative growth, flowering, and grain filling until maturity. The inclusion of detailed physiological processes enables the models to predict crop performance in environments that differ significantly from those in which the crop has traditionally been grown. This feature is particularly critical for assessing the implications of future climate scenarios, where past trends may no longer be reliable predictors. Another key feature is the integration of environmental data inputs. Crop simulation models require weather information such as temperature, rainfall, solar radiation, and humidity, as well as soil properties including texture, depth, organic matter, and water-holding capacity. These environmental variables strongly influence plant growth and yield outcomes. By incorporating them into simulations, the models can replicate the effects of climate variability and climate change on crop production. Many models also allow the incorporation of projected climate data from global and regional circulation models, thereby bridging the gap between climate science and agricultural planning. This integration makes crop simulation models particularly valuable as climate change assessment tools. Management practices represent another dimension captured by crop simulation models. Farmers make numerous decisions related to sowing dates, irrigation schedules, fertilizer application, pest control, and crop rotation. Simulation models can incorporate these decisions to test their impact on crop performance under varying climatic conditions. This capability allows the exploration of adaptation strategies to climate change, such as shifting planting windows, adopting new crop varieties, or optimizing irrigation practices. For example, models can assess whether earlier sowing can help crops escape terminal heat stress during grain filling, or whether supplemental irrigation at critical stages can mitigate drought effects. By simulating management interventions, these models help identify practices that increase resilience and reduce risks under changing climates. Calibration and validation are important features that enhance the reliability of crop simulation models. Calibration