Optimizing agricultural irrigation as virtual energy storage to match renewable power profiles unlocks climate...

Agricultural irrigation sustains food production and climate adaptation but intensifies energy use and greenhouse gas emissions. Incorporating irrigation into the power grid's demand-side response presents a promising yet underexplored opportunity for achieving energy and carbon co-benefits during the global energy transition. We develop the Irrigation Scheduling Optimization Model within the grain-water-energy-carbon nexus to align irrigation schedules with renewable-energy intermittency. Using China as a case study, we demonstrate that fine-tuning irrigation schedules reduces emissions by 11.1%-25.8% under current low-renewable penetrated grids and by 16.5%-56.9% as renewables penetration increases, by using up to 92.3% of otherwise curtailed renewable power. A combined strategy of energy transition, irrigation optimization and diesel-to-electricity electrification could achieve ~42.1 MtCO₂e (92.2%) of greenhouse gas savings by the 2050s, approaching net zero emissions. Efficacy peaks when local renewable shares reach 65%-70%, highlighting crucial spatiotemporal windows. Our study positions agricultural irrigation as a nature-integrated form of virtual energy storage, offering a pathway to enhance grid resilience and support low-carbon climate adaptation.