Crop simulation models are scientific tools designed to represent the growth and development of crops in relation to environmental conditions, management practices, and genetic characteristics. They integrate knowledge from plant physiology, soil science, meteorology, and agronomy to predict crop performance under varying conditions. These models use mathematical equations and algorithms to simulate processes such as photosynthesis, respiration, transpiration, nutrient uptake, and biomass accumulation. The ultimate aim is to provide insights into yield potential, resource use efficiency, and the likely impacts of climate variability and change. They are particularly important in climate change assessment as they allow researchers, policymakers, and farmers to explore adaptation strategies, evaluate risks, and optimize agricultural practices. One of the core features of crop simulation models is their process-based nature. They rely on a mechanistic understanding of plant-environment interactions rather than purely empirical data. For instance, models simulate how light interception by the crop canopy influences photosynthesis, how temperature affects crop phenology, and how soil moisture availability impacts transpiration and growth. By integrating these processes, they can capture non-linear responses that arise under stress conditions such as drought, heatwaves, or nutrient deficiencies. This is particularly valuable in assessing future scenarios where climatic conditions may fall outside the range of historical observations. Another key feature is their modular structure. Many crop simulation models are designed with separate modules for different processes, such as soil water balance, nutrient dynamics, pest and disease interactions, and crop growth. This modularity allows flexibility in tailoring the model to specific research needs. For example, a researcher interested in nitrogen management can focus on the soil-nitrogen-crop interaction module, while someone studying irrigation practices may emphasize the soil-water-crop module. This design makes the models adaptable and widely applicable. Crop simulation models are also characterized by their data requirements. To simulate accurately, they require detailed inputs such as daily weather data (temperature, rainfall, solar radiation, humidity), soil characteristics (texture, depth, organic matter, nutrient availability), crop management practices (sowing date, planting density, fertilizer and irrigation schedules), and genetic parameters of the crop variety. These inputs allow the models to capture the site-specific conditions that influence crop performance. While this makes them powerful, it also highlights a limitation: the need for accurate and high-resolution data, which is not always available in many regions. A further feature of crop simulation models is their ability to simulate scenarios and conduct sensitivity analyses. They can test how changes in one factor, such as sowing date or fertilizer application, influence crop yield under different climatic conditions. This makes them invaluable tools for assessing adaptation strategies in the face of climate change. Farmers and policymakers can use the results to identify practices that minimize risks and enhance resilience, such as shifting planting dates, adopting drought-tolerant varieties, or optimizing water use efficiency. Several well-established crop simulation models are widely used in climate change and agricultural research. The Decision Support System for Agrotechnology Transfer (DSSAT) is a suite