This study presents WaterCROPv2, an advanced agro-hydrological model designed to estimate irrigation water demand at a national scale by effectively balancing hydrological accuracy with manageable data requirements. Building on the original WaterCROPv1 model, WaterCROPv2 incorporates several significant enhancements, including hourly computation, rainwater canopy interception, soil-dependent leakage dynamics, and daily evapotranspiration trends based on localized meteorological data. A comparative analysis of both model versions demonstrates that WaterCROPv2 offers improved reliability in estimating irrigation water demand across various climatic regions, including both dry and wet areas.   When applied to maize cultivation in Italy during 2010, WaterCROPv2 aligned well with independent data from the Italian National Institute of Statistics (ISTAT), showing potentials for expansion to other countries. Notably, the relationship between estimation errors and cumulative precipitation reveals spatial variability: in wetter northern regions, the model slightly underestimates irrigation needs, while in the drier southern areas, it tends to overestimate demand.   Furthermore, WaterCROPv2 was used to assess mean irrigation water demand for maize from 2005 to 2015, illustrating its potential as a decision-support tool for policymakers. This analysis identifies optimal areas for maize cultivation and highlights necessary crop shifts. It also indicates where changes in irrigation systems could yield significant benefits and where investments are most feasible. Model simulations predict that transitioning to micro-irrigation could reduce national water demand by 21%, with reductions of 30-40% in regions with historically abundant water resources where inefficient irrigation practices are prevalent. However, only regions lacking existing irrigation infrastructure are likely to benefit from such system transitions.