Accurate crop yield prediction is paramount for food security amid climate volatility but struggles with complex, nonlinear, and spatially heterogeneous weather–crop interactions. This study develops a novel Spatially Heterogeneous Functional Additive Model (SH-FAM), representing a methodological innovation by uniquely integrating Multivariate Functional Principal Component Analysis (mFPCA) with data-driven climate zoning into a Generalized Additive Model (GAM) framework. The U.S. Midwest was selected as a study area for its pronounced east–west aridity and north–south thermal gradients, forming a natural laboratory for dissecting spatially heterogeneous climate–yield relationships. Unlike traditional models, SH-FAM preserves the continuous temporal structure of weather while allowing nonlinear biological thresholds to vary structurally across distinct agro-climatic zones. Extensive cross-validation shows SH-FAM reduces prediction error by 19% compared to benchmarks and substantially mitigates spatial bias during extreme events like the 2012 drought. We reveal distinct regional sensitivities to Heat and Drought Stress: water-limited western counties face immediate linear yield declines; the high-yielding core exhibits a nonlinear resilience threshold with catastrophic loss beyond a critical tipping point; northern regions show an inverted-U response where moderate warming enhances productivity. These spatially explicit response patterns enable zone-specific adaptation strategies, from drought mitigation in water-limited regions to thermal opportunity exploitation in heat-limited zones, providing actionable guidance for climate-resilient agricultural planning.
He et al. (Sat,) studied this question.