While soybeans are among the most consumed crops in the world, the majority of its production lies in hotspot regions in the US, Brazil and Argentina. The concentration of soybean growing regions in the Americas render the supply chain vulnerable to regional disruptions. In the year of 2012 anomalous hot and dry conditions occurring simultaneously in these regions led to low soybean yields, which drove global soybean prices to all-time records. Climate change has already negatively impacted agricultural systems, and this trend is expected to continue in the future. In this study we explore climate change impacts on simultaneous extreme crop failures as the one from 2012. We develop a hybrid model, coupling a process-based crop model with a machine learning model, to improve the simulation of soybean production. We assess the frequency and magnitude of events with similar or higher impacts than 2012 under different future scenarios, evaluating anomalies both with respect to present day and future conditions to disentangle the impacts of (changing) climate variability from the long-term mean trends. We find the long-term trends of mean climate increase the occurrence and magnitude of 2012 analogue crop yield losses. Conversely, anomalies like the 2012 event due to changes in climate variability show an increase in frequency in each country individually, but not simultaneously across the Americas. We deduce that adaptation of the crop production practice to the long-term mean trends of climate change may considerably reduce the future risk of simultaneous soybean losses across the Americas.