Integrating Climate Models and Coastal Risk Assessment in relation to Tropical Cyclones using an Adaptive Mesh Framework

Translating coarse-resolution climate projections into actionable, city-scale hazard information remains a critical challenge for coastal infrastructure planning worldwide. We present a transferable framework that combines adaptive-mesh numerical modeling with a physically consistent pseudo-global warming (PGW) methodology to generate high-resolution, climate-adjusted tropical cyclone (TC) scenarios. Here, we employ the CPAS (ClusterTech Platform for Atmospheric Simulation) model at variable resolutions (96-48-24-12-3 km), coupled with bias-corrected CMIP6 data under SSP5-8.5 forcing. Climate perturbations are applied using a physically consistent approach that also helps reduce model spin-up. The methodology incorporates a scale-aware physics scheme specifically validated for TCs. It bridges the scale gap between global climate models (~100 km) and decision-relevant hazard assessment (~1 km), offering a pathway applicable to coastal megacities globally. 

We demonstrate the framework using five representative TCs impacting the South China coast during 2008-2021, spanning a range of intensities, sizes, and approach characteristics. Historical control simulations accurately reproduce observed storm tracks and structures, establishing confidence in the climate-perturbed scenarios. Systematic climate change signals emerge across the event portfolio: (1) variable intensity amplification (3.1-8% °C⁻¹ climate sensitivity), dependent on storm structure, with the strongest storms exhibiting the largest response; (2) nonlinear precipitation enhancement, with median increases of 30-35% and amplification up to 50% at extreme percentiles; and (3) diverse structural responses, with some storms contracting while others expand their damaging wind field.

Event-to-event differences (e.g., initial intensity, storm size, track angle, and rapid intensification) drive diverse climate responses, making uniform adjustment factors potentially misleading. The framework provides physics-based, scenario-specific hazard simulations at 3 km resolution (extendable to < 1 km), directly linkable to exposure databases for “what-if” stress-testing of historical events under future climate conditions. Although demonstrated for TCs, the framework is transferable to other storm types and regions, with adaptive meshing enabling efficient, decision-relevant hazard modeling over complex coastal terrain.