Plant simulation models can help identify key physiological traits for targeted cultivar improvement. While promising physiological trait targets have been identified for rainfed maize and drought tolerance breeding, fewer studies have assessed irrigated maize while explicitly representing soil-plant nitrogen dynamics. We addressed this gap using the parametrized SSM-iCrop model in nitrogen-limited mode to represent irrigated production conditions. Building on reported genetic variation in maize, we assessed individual and combined physiological trait effects on grain maize growth and yield across four reference sites representing major irrigated grain maize production environments in Iran (> 55% of national grain maize area) during 2008-2022. Significant genotype-by-environment interactions were observed, with trait effect magnitude differing across sites. Enhanced radiation use efficiency (RUE) increased yields mainly in warmer sites, whereas accelerated leaf area development had a larger effect at the cooler site. Across key physiological trait groups (phenology, leaf area development and senescence, biomass accumulation and partitioning, yield formation, and nitrogen-related traits), accelerated leaf area development produced the largest yield gain (similar to 20% relative to the long-term mean yield of the standard cultivar SC704). Slower phenological development increased yield by 17%, while improvements in RUE and harvest index each increased yield by similar to 5%, relative to SC704. Integrating all beneficial single-trait improvements into a hypothetical genotype suggested an approximately two-fold increase in yield potential across Iran. These findings identify accelerated leaf area development as a promising breeding target for improving yield potential in irrigated maize systems. Given constraints on extending the growing period in many cropping systems, this trait may provide a practical pathway to increase yields, particularly in arid and semi-arid regions and other irrigation dependent maize production environments.