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The stability of the global food system is increasingly challenged as climate extremes driven by natural climate variability become more frequent and more likely to disrupt multiple major crop-producing regions at the same time. The Indian Ocean Dipole (IOD), El Niño-Southern Oscillation (ENSO), and North Atlantic Oscillation (NAO) are the main climate drivers that may influence regional weather conditions and further modulate crop productivity. However, it is still unclear how climate drivers have influenced crop productivity in historical periods and how their impacts may shift in the future. In this study, we integrate artificial intelligence algorithms with a large ensemble of process-based crop and climate models to evaluate the changing impact of climate variability on global crop productivity under greenhouse warming. We find that the NAO is projected to have a stronger influence on crop yields in the Northern Hemisphere and the ENSO increases dominance in the Southern Hemisphere under global warming. These shifting patterns are expected to expose an additional 5.1%–12% of global croplands to climate-oscillation-related disruptions. In addition, strong negative phases of NAO and El Niño events (strong positive phase of ENSO) are likely to cause simultaneous yield losses across multiple key food-producing regions. In contrast, their opposite phases do not demonstrate similar benefits, revealing an asymmetric influence in how these events affect food production and potentially heightening risks to global food security. Understanding these shifting climate signals is critical for building a more resilient food system. Our findings can help farmers and policymakers enhance early-warning systems and develop targeted adaptation strategies to increase food-system resilience and ensure the stability of global food-supply chains.
