Agricultural systems are highly vulnerable to flooding, particularly when extreme precipitation events occur during sensitive crop growth stages. However, most flood-risk assessments rely on static annual hazard scenarios and simplified design storms, neglecting the combined effects of hydrological seasonality, rainfall temporal structure, and crop phenology. In this study, we propose a probabilistic-hydrological-hydraulic framework for agricultural flood-risk assessment under seasonally varying short-duration extreme precipitation. The approach integrates non-asymptotic multivariate frequency analysis, copula-based dependence modelling among rainfall durations, stochastic microcanonical rainfall disaggregation, hydraulic simulations, and crop-specific flood depth-duration vulnerability functions. Flood-hazard maps are generated at the monthly scale and coupled with seasonally varying crop exposure and vulnerability conditions. The framework is applied to a flood-prone agricultural area in northern Italy. Results show that rainfall temporal structure and event duration substantially influence expected annual losses, with long-duration events generally producing the highest damages due to prolonged inundation and waterlogging conditions. The copula-based multi-duration analysis reveals a considerable uncertainty associated with inter-duration dependence, highlighting that the use of a single representative storm duration may significantly bias agricultural flood-risk estimates. The adopted microcanonical rainfall generator also proved effective in reproducing realistic multi-burst rainfall structures and temporally clustered events, which are particularly relevant for representing cumulative soil saturation and flood persistence processes. Overall, the proposed methodology provides a physically consistent and transferable framework for probabilistic agricultural flood-risk assessment and may support climate-risk analyses, adaptation planning, and flood-risk management in agricultural systems exposed to increasingly complex hydrometeorological extremes.