Compound flooding increasingly threatens developed coastal deltas. High risk pressure does not necessarily produce a resilience deficit where capacity is sufficient, whereas moderate-pressure areas may remain vulnerable when capacity is weak. Recent assessments increasingly integrate hazard, exposure, vulnerability, and adaptive capacity within unified risk frameworks such as the IPCC AR5 risk model. However, by collapsing these dimensions into a single composite risk score, such formulations cannot explicitly diagnose where—and by how much—compound flood risk pressure exceeds intrinsic resilience capacity, which is the information most directly needed for prioritizing resilience investment. This study diagnoses compound flood resilience deficits across Jiangsu Province, China, at county scale from 2000 to 2020. We introduce a diagnostic approach that separates risk pressure from intrinsic resilience capacity and quantifies their spatial mismatch. The risk pressure index is evaluated for consistency with observed disaster-loss indicators—direct economic loss and flood-affected area—over 2010–2020, and spatial statistics, time-series clustering, and explainable machine learning identify deficit patterns, pathways, and associated factors. Both the risk-pressure index and the derived deficit index are positively associated with observed losses, confirming that the framework captures major flood impacts. The resilience deficit index reveals persistent risk–resilience mismatch across Jiangsu. Three pathways emerge: capacity-buffered exposure, inland adaptive adjustment, and coastal resilience-deficit lock-in. Land-system conditions, communication access, transport connectivity, and economic recovery capacity are jointly associated with resilience deficits. The framework offers a transferable approach for prioritizing differentiated flood-risk management in coastal deltas.

Population growth can offset declining heating degree days, but its effect depends on where people live. The level of demographic detail required to represent this balance is still an open modelling choice. We compare fixed global population shares, updated country totals with fixed internal shares, and evolving gridded population under identical climate conditions. Crossing five climate models, three climate pathways, three population pathways and three base temperatures over seven decadal windows yields 945 experiments. Updating country totals reproduces the gridded global exposure to within 0.87 % in every post-baseline experiment. This agreement conceals substantial spatial divergence: late-century cellwise discrepancies reach median totals of 7.50–10.64% across paired pathways. Retaining global shares instead overestimates late-century exposure by 12.63–46.43% and reverses the projected direction in 347 of 810 post-baseline experiments. Decomposition attributes the contrast to the different roles of population growth and redistribution, while a conditional growth boundary explains why increasing population can coexist with declining exposure. These findings establish an outcome-specific basis for selecting population inputs: national updates capture the global heating-climate signal, whereas spatial redistribution remains essential for locating future exposure. The framework gives scenario designers an explicit criterion for deciding when a future population grid is required and when national totals are sufficient.