While optimizing the multidimensional built environment is increasingly advocated to address urban heat islands (UHI), current research predominantly relies on static, dimensionally fragmented evaluations at narrow spatial scales. Crucially, comprehensive evidence regarding how morphological associations dynamically evolve across heterogeneous baseline climates, socio-economic contexts, and distinct urbanization phases remain underexplored. Addressing these multidimensional gaps, we construct a decadal panel dataset (2011-2020) evaluating UHII across 325 Chinese prefecture-level cities by applying the unified 5D framework. Using explainable machine learning, we systematically decode morphological elements' nonlinear associations and their interactive patterns with overarching socio-environmental covariates. Results demonstrate these morphological associations are profoundly context-dependent, revealing the limitations of one-size-fits-all planning assumptions. First, background climate significantly contextualizes spatial thermal patterns; Notably, high-density development transitions from exhibiting a strong escalating thermal trajectory in arid regions to manifesting relatively lower thermal associations within specific density thresholds in wet zones. Second, morphological patterns exhibit strict stage dependency. As cities evolve from rapid physical expansion to refined stock optimization, structural layouts like polycentricity correlate with lower thermal loads, whereas background densification increasingly aligns with a weakened association between fragmented green spaces and relatively lower thermal predictions. Furthermore, socioeconomic contexts systematically define these boundaries: economic affluence emerges as a statistical condition correlating with compact development's environmental potential, while hyper-urbanization corresponds to weakened macro-ecological buffering and indicates that extreme spatial connectivity correlates with elevated thermal risks. Ultimately, drawing from a comprehensive suite of findings, this study advances framework integration and scale innovation, providing context-resolved evidence that supports a shift from universal targets toward localized, stage-calibrated, and socioeconomically aligned precision planning.