Climate-adjusted sequence learning reveals atmospheric-demand transitions in agricultural water–heat buffering

Agricultural systems are increasingly exposed to hotter and drier conditions, but it remains difficult to determine why some croplands stay greener, wetter, and cooler than others. Favourable crop conditions may simply reflect a favourable climate background, or they may indicate that an agricultural system performs better than expected under similar climatic stress. Here, we developed a climate-adjusted sequence-learning framework to separate these two situations across three major agricultural regions in the western United States. The framework first estimated the seasonal crop response expected under the observed climate conditions and then compared the observed vegetation, moisture, and surface-temperature trajectories with this expectation. The resulting residual trajectories were used to identify climate-adjusted water–heat response signatures, rather than being interpreted as direct causal evidence of intrinsic buffering capacity. We identified two dominant response signatures. Strong-response systems were generally greener, wetter, and cooler than expected under comparable climate conditions, whereas weak-response systems showed the opposite pattern. The difference between the two signatures was most evident during the middle to late growing season, when atmospheric water demand was high. As atmospheric demand increased, the mean buffer-advantage score declined from 0.204 to −0.456, the fraction of priority-vulnerable samples increased from 16.5% to 41.4%, and mean surface temperature increased from 19.4 °C to 47.0 °C. Higher precipitation did not fully offset this decline, suggesting that agricultural vulnerability depends on the balance between water supply and atmospheric demand rather than precipitation alone. Independent validation using OpenET evapotranspiration and Cropland Data Layer crop-type information further showed that the identified response signatures were consistent with agricultural water-use functioning. These findings show that the proposed framework can move beyond mapping where croplands look favourable to identifying where they perform better or worse than expected under comparable climate pressure. This provides a practical basis for prioritizing agricultural water-management interventions under rising atmospheric demand.