Amplified Urban Climate Extremes from Global Warming-Urbanization Synergy: A Physics-Informed Intelligence Paradigm

TL;DR

This paper introduces a physics-informed AI framework to better understand and predict how global warming and urbanization synergistically amplify climate extremes in cities.

Contribution

It proposes the Classification-Mechanism-Inference framework integrating typology, physics-informed machine learning, and risk projection for urban climate resilience.

Findings

Develops a global urban climate-morphology-development typology.

Creates physics-constrained surrogate models for nonlinear climate interactions.

Enables high-throughput, context-specific risk projections.

Abstract

The nonlinear synergy between global warming and urbanization is amplifying extreme climate risks in cities worldwide. While observations and simulations confirm these compounding effects, two fundamental bottlenecks impede predictive understanding: (1) fragmented, case-specific perspectives that hinder the discovery of universal mechanisms, and (2) a methodological divide between computationally prohibitive high-resolution models and AI-based tools that lack physical interpretability at urban scales. This article advocates for a paradigm shift toward the deep integration of physical principles with data intelligence. To this end, we propose a transformative "Classification-Mechanism-Inference" (CMI) framework. Classification involves establishing a global urban "climate-morphology-development" typology to enable systematic comparison beyond isolated case studies. Mechanism advocates for physics-informed machine learning (PIML) as the core engine to develop efficient, physics-constrained surrogate models for uncovering nonlinear interactions. Inference leverages these models for high-throughput, tailored risk projection to directly inform context-specific adaptation planning. The CMI framework aims to bridge the cognitive and methodological gaps, thereby advancing urban climate science from phenomenological description towards mechanistic, predictive, and decision-relevant science, which is crucial for building climate-resilient cities globally.