Agricultural producers around the globe are navigating increasingly complex decisions as they produce food and energy crops while facing climate anomalies, economic pressures, shifting agricultural policies, and environmental concerns. Routine decisions such as crop choice, soil preparation, fertilizer use, and irrigation scheduling have wide-ranging impacts on water availability, agricultural production and ecosystem health. Sustainable farm and water planning thus must consider not only local crop yields and socioeconomic drivers, but also competing user needs and multi-scale climate feedbacks. These concerns have created an urgent need for predictive models that can support field-scale agricultural decisions while explicitly simulating interactions between the climate and agrohydrosystem. In particular, such models need to be capable of forecasting the occurrence and impact of extreme events such as droughts under a nonstationary climate. This session highlights challenges and recent progress in the development of such models, specifically focusing on their application and testing in agricultural contexts.