The Global Risks Alliance (GRA)
A warming climate changes the frequency, intensity and location of natural catastrophes: heavier rainfall, more intense heat, shifting tropical cyclones and drier fuels for wildfire. Exposure growth in hazardous areas adds to the effect. This page sets out how climate change affects natural catastrophe risk, how models and pricing take account of it, what data exist and who decides.
GRA · In development · service level R0
Climate change and natural catastrophe risk describes how warming alters the frequency, intensity and location of perils such as floods, heat, cyclones and wildfire, alongside exposure growth in hazardous areas. Models increasingly condition hazard on current climate. Modellers, supervisors, planners and pool boards hold the decisions.
Which places drive loss in a severe year, and how far it reaches into reinsurance. Illustrative design values.
Changing physical hazards and nonstationarity. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Warming raises the probability of heavy rainfall and heat extremes and changes cyclone intensity. Models calibrated on past climate can understate current risk.
Population and asset growth in coastal and wildland areas raises losses even with no change in hazard. Separating exposure and climate effects is difficult.
Floods, severe convective storms and wildfire cause a rising share of insured losses and are less well modelled than primary perils.
Rising losses push premiums up and capacity out of some areas. Without risk reduction, insurability declines and protection gaps widen.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Secondary peril | A peril such as flood, hail or wildfire that is smaller per event than primary perils but frequent and rising in loss. |
| Loss normalisation | Adjusting past losses to current exposure and prices so events can be compared over time. |
| Climate-conditioned catalogue | A stochastic event set adjusted to current or projected climate rather than historical averages. |
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Normalised insured catastrophe losses | currency per year, adjusted for exposure | annual | Loss normalisation studies | ESTIMATED |
| Change in modelled average annual loss under climate scenarios | percent | per study | Climate-conditioned models | MODELLED |
| Frequency of heavy precipitation events | events per year | annual | Meteorological records | OBSERVED |
| Secondary peril share of insured losses | percent | annual | Industry loss records | ESTIMATED |
| Non-renewals in high-hazard areas | count of policies | annual | Regulator data | OFFICIAL |
| Population in high-hazard zones | persons | periodic | Hazard maps and census | ESTIMATED |
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| IPCC Sixth Assessment Report, Working Group I chapter on weather and climate extremes | Assessed changes in extremes and confidence levels | Free, IPCC terms |
| World Weather Attribution studies | Rapid attribution of extreme events | Free to read, terms to check |
| NGFS climate scenarios | Reference scenarios including physical risk | Free, NGFS terms |
| ERA5 reanalysis | Historical hazard climatology | Free and open, Copernicus licence |
| CMIP6 climate projections | Future climate scenarios | Free, terms per modelling centre |
| United States Treasury Federal Insurance Office and state regulator non-renewal data | Availability and price of homeowners insurance | Public domain or public records |
Reinsurance and Catastrophe prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Catastrophe modelling teams | Whether and how to condition hazard on current or future climate | Climate science, observed trends and model comparisons |
| Insurance supervisors | Whether climate is reflected in ORSA and capital | Supervisory climate scenarios and guidance |
| Land-use and building authorities | Where to build and to what standard | Hazard projections and loss evidence |
| Public pool and government reinsurer boards | How to price and fund rising risk | Climate-conditioned loss evidence |
Climate science, synthesised in international assessments, has established with high confidence that heat extremes have become more frequent and intense, and that heavy precipitation has increased in many regions. Sea-level rise increases coastal flood risk. For some perils, such as tropical cyclone frequency, trends are less certain, while evidence points to an increasing proportion of intense storms.
Natural variability, such as El Niño and La Niña, also affects hazard frequency year to year and can mask or amplify trends. Distinguishing signals from noise requires long records and physical understanding.
Losses have risen over time largely because of growth in exposed value, but hazard changes contribute for some perils. Models calibrated on long historical records may underestimate current risk where hazards have changed.
Statistical analysis of hazard records, using extreme value theory with nonstationary parameters. Short records limit power.
Estimating how climate change affected specific events, using climate science. Methods are evolving.
Adjusting event frequencies for the current or future climate. Adjustments carry large uncertainty.
Testing portfolios under climate scenarios. Scenarios are not forecasts.
Projections of hazard change carry uncertainty that grows with time horizon and is larger for some perils than others.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.