Wildfires, floods and droughts once regarded as statistical outliers are now recurring features of annual loss tallies. A humanitarian information portal reported that insurers covered about US$108 billion in disaster damage during 2025, keeping the industry above the US $100 billion threshold for another year.
Aon estimated that natural disasters caused US$260 billion in economic costs last year with nearly half transferred to insurers. The numbers underscore a structural shift in risk, not a string of anomalies.
This article explains how climate risk modeling sheds light on that shift. It outlines what the models do, why loss totals keep climbing and how these dynamics influence insurance availability, pricing and capacity. Understanding the modeling framework sets the stage for the discussion that follows on the forces reshaping today’s insurance market.
Defining Climate Risk Modeling
Climate risk modeling blends climate science, hazard datasets, exposure inventories and loss analytics to estimate how events such as heat-driven wildfires, riverine floods or prolonged droughts could affect assets, operations and the balance sheets that insure them. By translating meteorological projections into probability distributions of damage and downtime, the models provide a structured lens on future risk rather than a direct extrapolation of yesterday’s weather.
Insurers, reinsurers and corporate risk leaders rely on these models to test how portfolios perform under varied climate scenarios. As Munich Re notes in its analysis of wildfire risk, modeling accuracy demands constant refinement because a mix of human and natural factors shapes outcomes, especially in hotspots such as North America and Australia.
Connecting Hazard Data to Financial Exposure
Models start with peril-specific inputs: flame length and wind speed for wildfire, depth-duration curves for flood or soil-moisture deficits for drought. These hazard layers sit on high-resolution data about building construction, equipment values and process dependencies, generating a probabilistic view of direct damage and secondary impacts like business interruption. A single kilometer-wide flood crest, for instance, yields very different loss profiles for a data center versus a distribution warehouse even when water depth is identical.
Place-based analysis is therefore essential. Two facilities on opposite sides of a municipal boundary may face the same storm yet show divergent modeled losses because of differences in asset concentration, redundancy and critical-utility reliance.
The European Insurance and Occupational Pensions Authority (EIOPA) argues that accessible open-source tools can lower modeling costs and spur innovation, ultimately benefiting policyholders and supervisors alike.
Tracing Climate Events Into Insured Losses
Climate-related losses are no longer driven mainly by a few major hurricanes. Munich Re observed that “the big picture was alarming with regard to floods, severe convective storms and wildfires” and noted that these non-peak perils produced total losses of about US$166 billion, roughly US$98 billion of which was insured. The finding shows how hazards once considered secondary now shape industry-wide results.
Industry data indicate a persistent breach of the US$100 billion mark for annual insured catastrophe losses. Aon places 2025 global insured losses at roughly US$127 billion, continuing a five-year streak in that range and reinforcing the view that elevated payouts have become the baseline rather than an anomaly. Capital markets and rating agencies monitor this threshold closely because repeated crossings raise reinsurance costs and constrain capacity.
Explaining Wildfire Loss Escalation
Rising temperatures, prolonged droughts, wind-driven fire behavior and development in hazard-prone areas are increasing wildfire risk. These factors contribute to more frequent events, greater severity and higher insured losses. Munich Re notes, “Wildfires cause billions in losses every year, particularly in the United States. The costliest wildfires to date, which struck the Los Angeles area in January 2025, led to colossal losses totaling US$ 54bn.” The event challenged assumptions about winter wildfire risk. It also led insurers to reassess exposure limits and pricing in regions once considered lower risk during that season.
Floods impose a dual burden of direct property damage and cascading operational disruption when transportation, utilities and supply chains falter. Aon’s 2025 review highlights severe flood events across South and Southeast Asia as major drivers of uninsured economic loss, underscoring how inundation jeopardizes both physical assets and revenue continuity.
Drought functions as a hazard in its own right and as an amplifier of others. Extended dry spells sap agricultural output, strain cooling-water supplies for power plants and create combustible landscapes that elevate wildfire potential. The feedback loop between drought and fire magnifies volatility in regions already grappling with heat extremes and challenges underwriters trying to capture multidimensional exposure.
These event-level realities set the stage for a wider discussion of how recurring high-cost losses influence insurance pricing, policy terms and the availability of capital in an increasingly constrained market.
Explaining Insurance Market Tightening
Years of elevated catastrophe losses have forced carriers to rebuild capital, pay more for retrocessional protection and revisit the models that guide portfolio allocations. Munich Re concluded that weather-related events accounted for 97 percent of insured losses in 2025, reinforcing concerns that balance sheets are being tested by hazards once deemed manageable. As reinsurance costs rise in response, primary insurers face higher outlays to secure coverage layers and, in turn, pass those costs to policyholders through rate increases and stricter underwriting.
Understanding the Hardening of Terms and Capacity
Persistent climate-related payouts leave insurers cautious about geographic and peril concentrations. Deductibles for wildfire, flood and severe convective storm risks are increasing. At the same time, sub-limits are shrinking and exclusions for secondary perils are becoming more common. Aon reports that “the protection gap fell to a record low of 51%, due to the large concentration of losses in the U.S. which has higher insurance coverage” yet half of global economic losses remained uninsured, highlighting the ongoing need for innovative risk-transfer solutions.
Understanding What This Means for Insured Organizations
These pressures increase demand for more robust risk modeling and forecasting capabilities. At the same time, they expose the limitations of existing climate risk tools as insurers and organizations confront growing uncertainty.
Assessing Climate Modeling Limits and Uncertainty
Climate risk modeling is improving, yet uncertainties remain. Munich Re stresses that modelling wildfire risk is complex because “many man-made and natural factors” interact, an observation that extends to other hazards where evolving climate regimes challenge traditional assumptions about frequency and severity.
Recognizing the Limits of Historical Data
Historical datasets underrepresent the most damaging tail events, so models calibrated solely on past observations can understate exposure. Flood defenses, infrastructure plans and agricultural investments often rely on historical assumptions about weather patterns and hazard frequency. As climate-driven extremes become more severe and less predictable, those assumptions may no longer provide an adequate basis for decision-making.
Recognizing the Role of Model Assumptions and Tooling
Small errors in input data can cascade through complex climate systems, leading to diverging projections over multi-decade horizons. Tool access and transparency also matter. EIOPA’s case study on CLIMADA-App notes, “The CLIMADA-app is an open-source modelling tool” that helps stakeholders begin catastrophe modeling through an intuitive interface, demonstrating how open platforms broaden participation without prohibitive licensing costs. Other initiatives such as Oasis LMF, the Global Earthquake Model Foundation’s OpenQuake engine and the Network for Greening the Financial System (NGFS) Climate Impact Explorer further expand analytics options beyond proprietary software, allowing risk managers to benchmark vendor results and stress-test portfolios under multiple methodologies.
Recognizing these constraints doesn’t diminish the value of modeling; instead, it highlights the need to integrate model insights with engineering surveys, operational data and real-time monitoring, an approach explored in the next section on enterprise risk and resilience planning.
Linking Climate Models to Enterprise Risk and Resilience Planning
Robust climate modeling has evolved from a niche actuarial tool into a central pillar of enterprise risk management. Forward-looking hazard analytics now inform capital projects, supply-chain design and insurance program architecture, helping organizations see how heat, water and wind stress propagate through physical and financial systems.
Applying Models at the Site and Portfolio Level
At the facility scale, geospatial overlays pinpoint where hazard intensity intersects with asset value and critical infrastructure. Engineers layer fire-behavior indices, floodplain depths or drought scenarios onto maps of warehouses, substations and transportation nodes to identify clusters of exposure. Such mapping clarifies why a single distribution hub in a flood basin can dominate corporate loss potential while a nearby plant on higher ground registers only marginal risk.
Business interruption modeling extends the analysis from bricks to revenues. By linking probable hazard timelines to production cycles, logistics dependencies and customer demand patterns, analysts convert downtime into financial terms that resonate with treasury and board audiences. These estimates guide contingency planning, inform insurance structuring and support decisions on where to allocate resilience investment for maximum balance-sheet protection.
Positioning Sigma7 Within the Climate Risk Landscape
Here at Sigma7, we combine field-based engineering expertise with a global team of more than 200 specialists across 80 countries. This approach helps clients understand climate-related exposures across assets, operations and supply chains using hazard data, site assessments and supply-chain intelligence.
By linking climate hazard assessment with real-time threat monitoring on our S7 ONE platform, we align resilience strategies with measurable business outcomes. This fusion of scientific rigor and operational context shows why climate modeling has become indispensable to modern risk governance.
Interpreting What Climate Risk Modeling Signals for the Market Ahead
Aon’s catastrophe review concluded that “At US$41 billion, the Palisades and Eaton Fires accounted for a third of all insured losses in 2025 – marking the costliest wildfires on record globally.” The concentration of losses in secondary perils confirms what the models project: volatility is rising and high-severity events can emerge outside traditional peak-cat seasons.
When insurers, reinsurers and corporations run updated scenarios, the outputs point to a persistent elevation of baseline losses rather than an episodic spike. That outlook suggests continued upward pressure on premiums, tighter underwriting and sustained scrutiny of exposure data. Organizations that weave dynamic modeling into planning gain a clearer line of sight on capital needs, coverage structures and the financial implications of a changing climate.
For additional detail on incorporating climate risk assessment, resilience planning and insurance-related exposure analysis into enterprise risk programs, contact Sigma7.
