In January 2026, Aon published a finding that should have prompted immediate recalibration across every P&C pricing department in the country: severe convective storms have surpassed tropical cyclones as the costliest insured peril of the 21st century. Not in a single outlier year, but cumulatively. Since 2000, secondary perils including SCS, flooding, and wildfire have generated at least $1.56 trillion in industry insured losses, while perils traditionally treated as primary, such as tropical cyclones, earthquakes, and European windstorms, produced roughly $1.04 trillion (Aon 2026 Climate and Catastrophe Insight).
Three months later, the Q1 2026 data reinforced the pattern. Global insured catastrophe losses exceeded $20 billion, with a single SCS outbreak on March 10-12 driving $4 billion in insured losses and $5 billion in economic losses. The United States accounted for 79% of global insured losses in the quarter, approximately $16 billion, driven overwhelmingly by winter storm and severe convective storm activity (Aon Q1 2026 Global Catastrophe Report).
From tracking quarterly catastrophe loss reports across Aon, Gallagher Re, and Munich Re since 2020, the SCS trend line has been quietly steepening while industry attention fixated on named storms. The five-year average annual SCS loss to the U.S. property insurance industry now stands at roughly $32.5 billion, adjusted for inflation, compared to approximately $2.5 billion in the early 1980s (Munich Re). That is not a gradual drift. It is a structural shift in where insured losses actually concentrate, and it demands a corresponding shift in how P&C actuaries allocate their modeling, pricing, and reserving attention.
The SCS Loss Trajectory: How Severe Storms Surpassed Hurricanes
The overtaking did not happen in a single dramatic year. It accumulated through a relentless cadence of $40 billion to $60 billion SCS loss years that individually failed to trigger the industry alarm bells reserved for named hurricanes but collectively dwarfed the tropical cyclone total.
Consider the recent annual record. In 2023, SCS generated approximately $55 billion in global insured losses, shattering the previous record set in 2011 by almost $20 billion (Munich Re). In 2024, U.S. severe thunderstorms alone were responsible for $57 billion in total losses, of which $41 billion was insured, making it the second costliest year for SCS (Munich Re). In 2025, global SCS insured losses reached $61 billion, the third-highest annual total on record (Aon). The Insurance Information Institute (Triple-I) confirmed that U.S. SCS insured losses exceeded $50 billion for the third consecutive year in 2025, with total economic damages from tornadoes, hail, straight-line winds, and severe thunderstorms surpassing $68 billion.
The cumulative effect is decisive. While any single Category 4 or 5 hurricane can produce $30 billion to $100 billion in insured losses in a matter of days, such events remain episodic. The 2025 Atlantic hurricane season, for instance, produced below-average insured losses. SCS events, by contrast, grind through the loss budget quarter after quarter, year after year, with a frequency and geographic spread that makes them nearly impossible to avoid through portfolio concentration management alone.
| Year | U.S. SCS Insured Losses | Billion-Dollar SCS Events (U.S.) | Notable Context |
|---|---|---|---|
| 2023 | ~$55B | Record high | Previous all-time SCS record |
| 2024 | ~$41B insured / $57B total | Second-highest count | Second costliest SCS year |
| 2025 | $51B insured / $68B+ total | Third consecutive $50B+ year | EF5 tornado in North Dakota |
| Q1 2026 | ~$16B (all perils, 79% of global) | 5 events above $1B insured | Single $4B SCS event (March 10-12) |
This table understates the shift because it only captures the most recent years. The structural crossing point, where cumulative SCS losses since 2000 exceeded cumulative tropical cyclone losses, was confirmed by Aon's January 2026 annual report. The trajectory has been building for over a decade, masked by the outsized attention that individual named storms receive in cat model development, rating agency assessments, and reinsurance treaty negotiations.
Q1 2026 by the Numbers: SCS Dominates a Below-Average Quarter
The first quarter of 2026 was below average in aggregate but structurally revealing. Global economic losses reached $37 billion, well below the 21st-century Q1 average of $64 billion (Aon). Yet insured losses exceeded $20 billion, 6% above the historical average, reflecting the high insurance penetration of the perils that were active: SCS and winter storms in the United States (Aon Q1 2026 Global Catastrophe Report).
Twelve events each caused more than $1 billion in economic losses during the quarter, compared to a long-term average of nine. Five events each generated more than $1 billion in insured losses, against a long-term average of four. The most costly single event was the March 10-12 SCS outbreak, which produced $5 billion in economic losses and $4 billion in insured losses, a ratio that underscores the high insurance penetration of U.S. residential property markets exposed to convective storm risk.
Winter Storm Fern added another $4.6 billion in economic losses and $3.5 billion in insured losses, potentially ranking as the fourth-costliest U.S. winter storm on record. Combined, these two events alone accounted for roughly $7.5 billion in insured losses, more than a third of the global Q1 total.
The protection gap narrowed to approximately 46% in Q1 2026, down from the record-low 51% set for full-year 2025. This contraction reflects the geographic concentration of activity in the United States, where insurance penetration for wind and hail perils remains high relative to flood, earthquake, and perils dominant in developing markets (Aon).
Gallagher Re's parallel assessment estimated Q1 economic losses at a minimum of $58 billion, with insured losses of $20 billion. Their report noted this was 26% below the decadal average of $26 billion and 47% below the most recent five-year average, marking the fourth consecutive quarter with aggregated insured losses below $40 billion, the longest such stretch since early 2019 through mid-2020 (Gallagher Re Q1 2026 Natural Catastrophe and Climate Report). That relative calm is precisely why the SCS signal matters: even in a "quiet" quarter, convective storms drove the majority of insured losses.
Carrier-Level Evidence: Allstate's $925 Million March Signal
Aggregate industry data tells one story. Carrier earnings tell a more granular one. Allstate reported $1.24 billion in pretax catastrophe losses for Q1 2026, with March accounting for the bulk. Fifteen wind and hail incidents in March generated approximately $925 million in losses, with three events contributing roughly 80% of that total (Allstate Q1 2026 catastrophe disclosure). Winter Storm Fern added another $138 million earlier in the quarter.
State Farm, the largest U.S. homeowners insurer, reported receiving over 53,000 home and auto claims from storms as of March 16, 2026 alone, with the highest claim counts concentrated in Illinois, Ohio, and Missouri. Hail and wind damage were the most common causes of loss.
These carrier-level disclosures matter for actuarial analysis because they reveal how SCS losses distribute across personal lines books. Unlike a landfalling hurricane that concentrates losses in a defined geographic cone, SCS events scatter across the Midwest, Southeast, and increasingly the mid-Atlantic in patterns that challenge traditional geographic diversification assumptions. A carrier with heavy Texas, Illinois, and Ohio exposure, three of the states most frequently hit by SCS, cannot diversify away from this peril without fundamentally restructuring its book.
Why Catastrophe Models Underweight SCS Relative to Actual Loss Contribution
The gap between SCS's actual loss share and its treatment in catastrophe models is the central actuarial problem. Several structural factors explain the disconnect.
Observational bias and data limitations. The Storm Prediction Center (SPC) database, which forms the foundation of U.S. severe weather climatology since the 1950s, contains systematic reporting biases. Storms in densely populated areas generate many reports from trained spotters and storm watchers. Storms in rural areas, particularly small-to-medium-sized events across the Plains and upper Midwest, go underreported or unreported entirely. This creates a population bias that inflates apparent frequency near cities while understating overall event counts. As Moody's RMS documented, lower population density in the Plains results in greater undercounting during summer months, and southeastern tornado activity in spring produces more reliable observations than Northern Plains activity in summer due to regional population distribution (Moody's).
Calibration vintage lag. Research presented at the CAS Spring Meeting found that data used by industry-leading SCS catastrophe models to calibrate their event sets only ran through the 2010s, after which SCS events escalated to new records. This means the models that most carriers rely on for pricing were calibrated before the three consecutive $50 billion+ loss years of 2023-2025. Guy Carpenter research found a 4.6% variance between expected and actual losses over the 1990-2023 period, a gap that has widened considerably in the most recent years (CAS Actuarial Review).
Hazard resolution limitations. SCS damage can vary dramatically from block to block within the same event. A hailstorm that drops 3-inch stones on one subdivision may produce only rain two miles away. Legacy catastrophe models with coarse hazard resolution produce unrealistic event footprints, simplified vulnerability curves, and financial modules that overlook critical policy terms like percentage deductibles for wind and hail (Moody's RMS).
Institutional allocation of modeling resources. Historically, the catastrophe modeling industry and the carriers that fund it have directed the largest share of R&D spending toward tropical cyclone and earthquake models, treating SCS as a "secondary" peril despite its now-dominant loss share. This created a self-reinforcing cycle: better hurricane models produced more precise hurricane loss estimates, which attracted more reinsurance capacity and more sophisticated treaty structures for that peril, while SCS pricing relied more heavily on historical experience rating and judgment.
New Models Close Part of the Gap
The modeling industry has begun to respond. Moody's RMS launched its North America Severe Convective Storm HD Models in December 2025, introducing a fully re-engineered vulnerability module calibrated against over $55 billion in location- and policy-level claims data and validating more than 2,700 property damage curves. The models use machine learning trained on densely populated regions to correlate storm reports with convective conditions, then apply expected undercounting levels across the entire country to fill observational gaps (Moody's RMS).
Cotality (formerly CoreLogic) published its 2026 Severe Convective Storm Risk Report in March, finding that 43.5 million U.S. properties face moderate or greater hail risk, representing $17.84 trillion in reconstruction cost value (RCV). In 2025 alone, Cotality's Weather Verify system estimated that damaging hail of 2 inches or greater impacted more than 600,000 single- and multifamily homes. The U.S. recorded 142 days with damaging hail in 2025, seven more than in 2024 and well above the 20-year average of 122 days (Cotality).
KatRisk has also released updated SCS models focused on precision hail mapping, and the NAIC has noted significant updates to SCS models across the vendor landscape, with the expectation that insurers will begin relying on these models more frequently and that such models will increasingly appear in rate filings (NAIC Spring 2026 National Meeting).
These model updates represent genuine progress. But they also create a near-term actuarial challenge: as carriers adopt the new models, indicated rates for SCS-exposed territory will likely increase, in some cases substantially. The gap between legacy model outputs and updated model outputs represents the accumulated underpricing that has built up over the prior model generation.
Hail: The Dominant Loss Driver Within SCS
Within the SCS peril category, hail is the single largest driver of claims and losses. The Triple-I reported that hail accounts for as much as 80% of severe convective storm claims in any given year, with roof damage representing an estimated 70% to 90% of total insured residential catastrophe losses.
Cotality's analysis reinforces the scale of the exposure. At the 1-in-500-year return period, hail alone could generate approximately $58 billion in insured losses, representing roughly 80% of the estimated $71 billion in total SCS peril losses at that return period. At a 1-in-100-year level, a severe hailstorm could produce approximately $30 billion in insured losses, comparable to a major hurricane (Cotality 2026 SCS Risk Report).
Texas stands out as the most concentrated exposure. More than 235,000 homes in the state experienced damaging hail in 2025, the highest in the nation. The Texas Triangle, encompassing the Dallas-Fort Worth, Houston, Austin, and San Antonio metro areas, accounts for more than $2.2 trillion in reconstruction cost value tied to moderate or greater hail risk. Wyoming (41,000+ impacted homes), Oklahoma, Wisconsin, and Kansas round out the most affected states (Cotality).
The 2025 season also included a notable outlier: an EF5 tornado struck North Dakota, ending a 12-year gap since the last most violent tornado classification in the United States. While EF5 events are rare and do not drive aggregate loss trends, this event highlighted the expanding geographic and intensity envelope of severe convective activity.
The Pricing and Reserving Challenge for P&C Actuaries
For pricing actuaries, SCS presents a fundamentally different challenge than hurricane. Hurricane pricing relies on catastrophe model output supplemented by historical experience. The models are mature, regularly updated, and calibrated against decades of detailed event data. The reinsurance market offers well-structured excess-of-loss treaties, cat bonds, and ILS capacity specifically designed for tropical cyclone exposure.
SCS pricing, by contrast, has historically relied more heavily on actuarial experience rating, with catastrophe models playing a supplementary rather than primary role. Research presented at the CAS Spring Meeting identified four ratemaking methodologies currently in use:
1. Catastrophe adjustment factors. The simplest approach, applying factors to non-catastrophe projected loss costs based on historical catastrophe-to-non-catastrophe loss ratios. This method is unresponsive to geographic exposure shifts and coverage changes.
2. Catastrophe losses to amount of insurance years. Analyzing catastrophe losses relative to insured value exposure over time, with appropriate trend factors applied. This overcomes some Method 1 limitations by reflecting building stock value changes.
3. SCS pure premium analysis. Separating frequency and severity components for targeted adjustments. Rarely used historically due to data requirements, this approach is primarily adopted by larger carriers with sufficient granular claims data.
4. Scaled catastrophe model outputs. Using catastrophe model average annual loss (AAL) estimates but scaling them based on observed differences between modeled and actual losses. This became more common as models matured but is only as good as the underlying model calibration, which, as discussed, lags the current loss environment.
The CAS research concluded that no single method is sufficient to ensure adequate pricing for SCS. Pricing actuaries need to consider multiple methods and work with the internal and external data their carriers can obtain (CAS Actuarial Review).
For reserving actuaries, the challenge is different but related. SCS losses develop relatively quickly compared to long-tail casualty lines, but the sheer volume of claims from a single multi-state outbreak can overwhelm adjusting capacity and delay settlements. The 53,000+ claims State Farm received from storms in a single two-week period in March 2026 illustrates the operational strain. Reserve adequacy analysis must account for both the frequency of SCS events, which is trending upward, and the severity per claim, which is being driven higher by construction cost inflation, labor shortages in the roofing trades, and litigation trends in several SCS-prone states.
Non-Weather Factors: 90% of the Loss Growth Story
One of the most important findings from recent SCS loss research challenges the assumption that worsening weather alone explains the loss trajectory. According to data from Gallagher Re, non-weather factors account for up to 90% of SCS loss growth since 2000. These factors include:
Population migration into high-risk areas. Urban and suburban development in SCS-prone regions has increased by 20% since 2000. The Texas Triangle, central Oklahoma, and the Nashville-Memphis corridor have all experienced rapid housing growth in areas with historically high SCS frequency.
Construction cost escalation. The cost to replace a roof, the single largest SCS loss component, has increased dramatically. Material costs, labor shortages, and building code upgrades all contribute to higher severity per claim.
Legal system costs and litigation trends. Several SCS-prone states, including Texas, Florida, and Colorado, have experienced surges in assignment-of-benefits abuse, public adjuster involvement, and attorney representation on residential property claims. These factors inflate average claim costs well beyond the underlying physical damage.
Insurance-to-value gaps. Many homeowners policies were written at replacement cost values that have not kept pace with actual construction costs, creating underinsurance that manifests as larger-than-expected losses when revaluation occurs at renewal or loss.
The Triple-I reinforced this finding, noting that every $1 invested in hazard mitigation can save up to $33 in future disaster costs. This ratio underscores the economic efficiency of mitigation programs relative to continued absorption of rising losses through premium increases alone.
Regulatory and Mitigation Responses
The NAIC restructured its catastrophe-related working groups at its Fall 2025 National Meeting, consolidating the Climate and Resiliency Task Force, the Catastrophe Insurance Working Group, and the FEMA Working Group into a single Natural Catastrophe Risk and Resilience Task Force. Two new subgroups emerged with more defined mandates: the Pre-Disaster Mitigation and Risk Modeling Working Group, and the Severe Peril Working Group.
The Pre-Disaster Mitigation Working Group's charter includes creating and coordinating resilience tools to assist state regulators in developing state-based mitigation grant programs. The Severe Peril Working Group will focus specifically on the growing SCS challenge, including evaluating how updated catastrophe models will appear in rate filings and whether current regulatory review processes are calibrated to assess model-driven rate indications for SCS exposure (NAIC Spring 2026 National Meeting).
At the federal level, the bipartisan Strengthen Homes Act and related mitigation legislation would create grant programs to fund impact-resistant roofing and other structural upgrades in SCS-prone areas. The economic case is compelling: Cotality's analysis shows that 76 million homes face moderate or greater tornado risk ($27 trillion RCV) and 64 million homes face moderate or greater straight-line wind risk ($23 trillion RCV). Even modest penetration of mitigation programs could meaningfully reduce the aggregate loss burden.
For actuaries, the intersection of regulatory modernization and mitigation investment creates both challenges and opportunities. Rate filings that incorporate updated SCS models will face regulatory scrutiny. Carriers that can demonstrate mitigation credits, where policyholders have installed impact-resistant roofing or other protective measures, may find a more receptive regulatory environment for overall rate adequacy.
Reinsurance Market Implications: SCS as Attritional Drag
From a reinsurance perspective, the SCS trend creates a distinct challenge. Traditional property catastrophe excess-of-loss treaties are designed to respond to low-frequency, high-severity events, the paradigmatic hurricane or earthquake. SCS events, by contrast, tend to produce frequent, moderate-severity losses that erode retained earnings but rarely pierce high attachment points.
Gallagher Re's April 2026 First View estimated that a single event or series of events producing insured losses of $115 billion to $125 billion above expected average annual catastrophe losses would be needed to meaningfully shift property reinsurance pricing trajectories. SCS losses, even at the current elevated levels, fall well below this threshold individually. They accumulate as attritional drag on primary carrier profitability rather than as treaty-triggering events.
This structural mismatch means that the rising SCS loss trend is predominantly a primary carrier problem. Reinsurers benefit from higher ceding premiums driven by SCS-related rate increases but bear limited loss exposure unless aggregate stop-loss or quota share structures are in place. The pricing signal from the reinsurance market, which has historically been the strongest external pressure for primary carrier rate adequacy, is muted for SCS relative to its actual loss contribution.
For cedants evaluating their catastrophe reinsurance programs, this suggests that aggregate excess-of-loss covers, which respond to the accumulation of smaller events across the year, may be more appropriate for SCS exposure than traditional per-occurrence towers. The growth of ILS and cat bond capacity that covers aggregate triggers rather than single-event triggers aligns with this structural need.
Why This Matters for Actuarial Practice
The reclassification of SCS from secondary to primary peril status is not merely an academic reclassification. It has direct implications for how P&C actuaries structure their work.
Cat model reliance. Actuaries who embed catastrophe model output into their pricing indications without adjustment for the SCS calibration gap are systematically underpricing the peril that now drives the most insured losses. The transition from legacy models to the updated Moody's RMS, Cotality, and KatRisk models will produce discontinuities in indicated rates that must be communicated clearly to underwriting and management.
Loss trend selection. SCS loss trends cannot be set using the same framework applied to attritional non-cat losses. The 13-fold increase in five-year average losses from the early 1980s to today, from roughly $2.5 billion to $32.5 billion (Munich Re, inflation-adjusted), reflects a combination of exposure growth, construction cost escalation, litigation trends, and potentially shifting weather patterns. Disentangling these components is essential for selecting defensible trend factors.
Reserve adequacy monitoring. The frequency of SCS events means that IBNR development patterns for catastrophe losses are compressed relative to hurricane, where a single large event dominates the year. Actuaries monitoring reserve adequacy must track SCS event frequency and severity separately from hurricane and earthquake reserves to avoid masking deterioration in one peril with favorable development in another.
Capital modeling. Internal capital models and rating agency capital adequacy frameworks that allocate the majority of natural catastrophe risk charge to tropical cyclone and earthquake may understate the true risk profile of carriers with significant SCS exposure. This is particularly relevant for regional and super-regional carriers concentrated in the Midwest and Southeast, where SCS is the dominant catastrophe peril.
The industry's collective challenge is clear: the peril that now generates the most insured losses globally is the one that has historically received the least modeling investment, the least reinsurance market attention, and the least actuarial specialization. Closing that gap is the defining P&C actuarial challenge of the next decade.