Overlooked yet influential functional mixing shapes urban precipitation extremes across environmental gradients

Extreme precipitation is intensifying and is challenging climate adaptation in cities. Existing urban form research often focuses on isolated physical attributes and rarely integrates functional heterogeneity into planning diagnostics. We propose a holistic urban fabric framework that integrates five metrics that capture overall construction volume, morphological compactness, vertical filling, porosity, and functional mixing. Using multisource panel data for 225 Chinese cities from 2005 to 2018, we combine trend tests, state space causality analysis, and interpretable machine learning to identify predictive causal imprints, nonlinear response regimes, and environmental modulation across environmental gradients. Across three precipitation extreme indicators, functional mixing shows the most consistent signal among the five dimensions, including the highest imprint strength and top importance rankings. We distill the metrics into planning-relevant governance dimensions, define four regional typologies, and provide a process-oriented governance framework that supports surface-flux management, ventilation-oriented morpho-dynamic design, and structural regime governance.