Why Cat Models Ignore NOAA's Below-Normal Hurricane Call: Seasonal Forecasts vs. Multi-Year Loss Curves

From tracking the disconnect between seasonal forecasts and cat model outputs across six consecutive Atlantic hurricane seasons, the pattern is clear: one-year outlooks move headlines but not model loss curves. NOAA released its 2026 Atlantic hurricane season outlook on May 22, forecasting 8 to 14 named storms with a 55% chance of below-normal activity. Colorado State University's independent forecast aligns, calling for 13 named storms, 6 hurricanes, and 2 major hurricanes. Both teams point to the same driver: a robust El Niño expected to increase vertical wind shear across the Atlantic basin during peak season.

This is the first below-normal seasonal call since 2015. That alone makes it newsworthy. But for actuaries pricing property catastrophe risk, managing reinsurance programs, or setting PML estimates, the seasonal number matters far less than the multi-decade frequency and severity distributions embedded in commercial cat models. Those models do not update their stochastic event sets because NOAA expects fewer named storms this year. They calibrate to long-term climatology. And the exposure base those models run against has grown by trillions of dollars since the last below-normal forecast.

55%

Probability of Below-Normal Season (NOAA)

$12.3T

Coastal Property Value Exposed to Hurricane Risk

14.7%

Property Cat Reinsurance Rate Decline (Jan 2026)

$4.3B

TWIA 2026 PML (Four-Model Blend)

What NOAA's Forecast Actually Says

The specifics of NOAA's May 22, 2026 outlook deserve careful reading, because the headline "below-normal" obscures important nuance. NOAA assigned a 55% probability to below-normal activity, a 35% chance of near-normal, and a 10% chance of above-normal. The named storm range of 8 to 14 compares against a 30-year average of 14 named storms, 7 hurricanes, and 3 major hurricanes.

The below-normal call rests on three atmospheric signals. First, El Niño conditions carry an 82% probability of developing between May and July 2026, with NOAA's Climate Prediction Center expecting the signal to strengthen through peak hurricane months. El Niño typically increases vertical wind shear across the tropical Atlantic and Caribbean, which inhibits cyclone organization and intensification. Second, Atlantic sea surface temperatures are expected to be slightly above normal but not at the record levels seen in 2024 and 2025. Third, trade winds across the basin are projected to be weaker than average, a mixed signal that partially offsets the shear advantage.

Within those ranges, NOAA projects 3 to 6 hurricanes and 1 to 3 major hurricanes (Category 3 or stronger). CSU's independently derived forecast is modestly more specific: 13 named storms, 6 hurricanes, 2 major hurricanes, and an accumulated cyclone energy of 95 (below the 1991-2020 median of 123). CSU places the probability of a major hurricane striking the U.S. coastline at 32%, below the long-term average of 43%, and the probability for the Gulf Coast at 20%, below the 27% historical base rate.

These are conditional probability statements, not guarantees. NOAA itself emphasizes that the outlook does "not indicate where or when any storms may affect land." The entire framework predicts basin-wide activity; it says nothing about the spatial distribution of tracks or the likelihood of landfalls in high-exposure corridors.

Why Cat Models Operate on a Different Clock

Commercial catastrophe models from Verisk, Moody's RMS, Impact Forecasting (Aon), and Cotality calibrate their hurricane hazard modules to multi-decade or century-scale climatological records. Verisk's Touchstone platform, for instance, draws on historical storm data extending back to the mid-1800s and supplements it with synthetic tracks generated from physics-based simulations of atmospheric and oceanic conditions. The stochastic event set that emerges represents a probability-weighted view of what can happen over a long time horizon, typically expressed as average annual loss, occurrence exceedance probability, and aggregate exceedance probability curves.

None of those outputs shift because a particular year's seasonal forecast is above or below average. The reason is structural: cat models are designed to price risk across multi-year policy periods and reinsurance treaty terms, not to reflect the conditions of any single hurricane season. A five-year reinsurance contract or a 30-year mortgage default probability does not change because NOAA expects El Niño to suppress activity for one season.

This creates a persistent disconnect that confuses market participants who conflate seasonal forecasts with risk assessments. When NOAA says "below-normal," it means the basin is likely to produce fewer named storms than the 30-year average. When a cat model says the 1-in-100-year probable maximum loss for a Florida portfolio is $8 billion, it means that across thousands of simulated seasons incorporating the full range of climatological variability, there is a 1% annual probability of that loss being exceeded. The model already accounts for El Niño years, La Niña years, and neutral years in its simulation framework.

TWIA's Four-Model Blend Illustrates the Approach

The Texas Windstorm Insurance Association (TWIA) provides a rare public window into how cat models drive actual purchasing decisions. For the 2026 hurricane season, TWIA's actuarial committee selected four commercial cat models: Verisk Touchstone version 13, Moody's RMS RiskLink version 25, Impact Forecasting version 18, and Cotality RQE version 25. The committee weighted the highest and lowest model outputs at 20% each and the two middle figures at 30% each, producing a blended 1-in-50-year PML of $4.3 billion.

Critically, TWIA's committee recommended using "long-term hurricane frequency assumptions" rather than any near-term forecast adjustment. The committee also applied a 15% loss adjustment expense load on top of the modeled loss. This decision explicitly decouples the PML estimate from the seasonal outlook: whether NOAA forecasts 8 storms or 18 storms, TWIA's cat model blend produces essentially the same PML, because the underlying stochastic catalogs reflect the same long-term frequency distribution.

The divergence between vendor models is itself instructive. While the specific outputs are not published, the weighting methodology (20/30/30/20) reveals that the four models do not agree on a single PML figure. That disagreement reflects different approaches to track modeling, wind field parameterization, vulnerability functions, and demand surge assumptions. Compared to the previous year's 1-in-100 PML of $6.2 billion, the shift to a 1-in-50 standard under Texas legislative changes reduced the purchasing threshold, but the models' underlying loss distributions remain calibrated to the full historical and simulated record.

The Exposure Problem: $12.3 Trillion and Climbing

Even if every seasonal forecast model were perfectly accurate and the 2026 Atlantic season produced the fewest storms in a decade, the insured loss potential from any single landfall would still be higher than it was a year ago. The reason is exposure accumulation. CoreLogic estimates that more than 32.2 million homes across 20 states face moderate or greater risk from hurricane winds, representing over $12.26 trillion in reconstruction cost value. Storm surge threatens an additional 6 million homes nationwide, accounting for $2.1 trillion in risk.

The concentration of that exposure in a handful of coastal corridors amplifies the problem. Florida alone accounts for 8.25 million homes at wind damage risk and 2.47 million at storm surge risk, with surge exposure representing $747.6 billion in replacement cost, more than three times the exposure of Louisiana, the second most at-risk state. New York carries nearly $9 trillion in total insured property exposure, with $6 trillion concentrated in coastal counties. KCC (Karen Clark & Company) analysis published in April 2026 found that a 1-in-100-year hurricane making landfall near Rockaway Beach could generate more than $100 billion in insured losses in New York alone.

These figures grow annually. Construction cost inflation, replacement cost escalation, and continued development in hurricane-prone counties all push insured values higher. Trade press estimates put the annual growth rate for insured values in hurricane-exposed coastal counties at 6% to 10%, depending on the state and the specific valuation methodology. When cat models update their exposure databases to reflect current replacement cost values, the same storm simulated on a 2026 exposure base produces a larger loss than it would have on a 2024 base, regardless of what the seasonal forecast says about storm frequency.

State	Homes at Wind Risk	Homes at Surge Risk	Surge Exposure (RCV)
Florida	8.25M	2.47M	$747.6B
New York	2.8M	1.1M	$530B+
Louisiana	1.9M	0.8M	$225B
Texas	4.6M	0.6M	$195B
All 20 States	32.2M	6.0M	$2.1T

Historical Precedent: Below-Average Storm Counts, Record Insured Losses

The assumption that fewer named storms means fewer insured losses has been tested and disproven multiple times. Two seasons in particular illustrate the fallacy.

The 2004 Atlantic season produced 15 named storms, which was near the long-term average. But four of those storms, Charley, Frances, Ivan, and Jeanne, struck Florida within a six-week window. Charley alone generated $8.2 billion in insured losses, Ivan produced $7.8 billion, and Frances added $5 billion. The season's aggregate insured losses exceeded $26 billion in 2004 dollars, making it one of the costliest seasons on record up to that point. The named storm count was unremarkable; the spatial clustering of landfalls in a single high-exposure state was what drove the losses.

The 2017 season tells the same story at a larger scale. Hurricanes Harvey, Irma, and Maria collectively produced $93 billion in insured losses across North America and the Caribbean. Harvey's $133.8 billion in total economic damage made it one of the four costliest storms in U.S. history on an inflation-adjusted basis. The 2017 season had 17 named storms, which was above average, but the critical variable was not the count; it was the fact that three major hurricanes made landfall in quick succession across Texas, Florida, and Puerto Rico.

The 2015 season, the last time NOAA issued a below-normal forecast, ended up producing 12 named storms, above the 10 that NOAA had forecast as the midpoint. No major hurricanes struck the United States that year, but the season demonstrated that below-normal forecasts are conditional probability estimates with meaningful uncertainty bands. The 2025 season was forecast as near-average and produced 13 named storms, including three Category 5 hurricanes, though none made U.S. landfall at peak intensity.

The lesson for actuaries is that the loss distribution conditional on a below-normal forecast is not simply a downward-shifted version of the unconditional distribution. The tail risk, a single major hurricane tracking through a densely insured metropolitan corridor, persists at levels that matter for capital adequacy and reinsurance purchasing decisions.

Reinsurance Pricing Is Already Moving, Forecast or Not

The reinsurance market entered 2026 with pricing momentum that has nothing to do with seasonal hurricane forecasts and everything to do with capital supply. Global reinsurer capital reached a record $785 billion by the April 2026 renewal, supported by strong retained earnings from consecutive profitable years and a surge in alternative capital that pushed ILS capacity to $136 billion. Catastrophe bond issuance in Q1 2026 hit $6.7 billion, with the total outstanding market reaching $63.9 billion.

That capital abundance drove the sharpest rate decline in over a decade at the January 2026 renewals. Howden Re reported risk-adjusted global property-catastrophe reinsurance rates-on-line fell 14.7%, with direct and facultative down 17.5% and retrocession down 16.5%. Retrocession capacity "comfortably exceeded demand," with buyers considering up to $800 million in additional limits. Regional breakdowns showed U.S. property cat rates declining 10% to 20%, European rates falling 10% to 20%, and Asia Pacific loss-free programs softening by similar margins.

The June 1 renewal, which sets the terms for the hurricane season, continued the trend. AM Best projected that Florida domestic carriers would secure double-digit rate decreases on higher-layer catastrophe reinsurance. Howden Re's June data showed property cat rates-on-line down between flat and 20% depending on program structure and attachment point. Citizens Property Insurance Corporation called $1.1 billion in outstanding Everglades Re II Series 2024-1 catastrophe bonds and replaced them with $600 million in new bonds at spreads approximately 30% below the 2024 levels, saving roughly $67 million annually on the replacement tranche alone.

None of these pricing movements were triggered by the NOAA forecast. The softening began at the January renewal, months before the seasonal outlook was released. Capital supply, retained earnings, low 2025 catastrophe losses, and Florida's tort reform validation all preceded and drove the rate decline. The below-normal forecast is being absorbed into a market that was already repricing downward.

The One-Storm Problem: Why Frequency Forecasts Understate Severity Risk

Seasonal forecasts estimate the expected number of named storms, hurricanes, and major hurricanes across the entire Atlantic basin. They say almost nothing about severity conditional on landfall, which is what drives insured losses. A below-normal season with 9 named storms can easily produce a single Category 4 landfall in the Miami-Dade corridor that generates $50 billion or more in insured losses. An above-normal season with 20 named storms, most of which recurve into the open Atlantic, might produce minimal insured losses.

This is the one-storm problem, and it is the fundamental reason that cat models ignore seasonal forecasts. The loss distribution for property catastrophe insurance is dominated by the tail: the rare but extremely costly events where a major hurricane tracks through a densely insured urban area at peak intensity. Reducing the expected number of storms from 14 to 10 has a modest effect on the expected loss, but it has almost no effect on the tail of the distribution, because the scenarios driving the 1-in-100 and 1-in-250 return periods involve specific track and intensity combinations that can occur in any season, whether the overall activity level is high or low.

KCC's April 2026 analysis quantified this directly for the New York metropolitan area. A 1-in-100-year hurricane tracking near Rockaway Beach would generate more than $100 billion in insured losses. That scenario is in the stochastic catalog regardless of basin-wide activity. The 1938 Great New England Hurricane, which brought over 15 feet of storm surge to Long Island as a Category 3 system, occurred in a season with only 6 named storms. Seasonal activity was well below average; the single storm that mattered produced catastrophic losses.

How the Disconnect Plays Out in Actuarial Work

For pricing actuaries, the practical implication is that the seasonal forecast should not alter the catastrophe load in rate filings. The cat load is driven by modeled average annual loss (AAL) from the stochastic event set, not by a single season's expected frequency. When the reinsurance treaty renews at lower rates, the cat load cession credit changes, and that change flows through to the filed rate. But the underlying modeled loss does not change because the atmospheric conditions this year favor El Niño suppression.

For reserving actuaries, the seasonal outlook creates a narrative risk. Favorable forecasts can lead management to pressure reserving actuaries to release catastrophe reserves or reduce IBNR loads during the season. Patterns from tracking this across multiple soft-market years suggest the opposite discipline is warranted: soft-market reinsurance pricing and benign seasonal forecasts are precisely the conditions that preceded material reserve shortfalls in prior cycles. The 2004-2005 hurricane seasons followed a period of rate softening and produced catastrophe losses that depleted reserves across the Florida domestic market.

For enterprise risk management, the tension between the seasonal forecast narrative and the cat model view creates communication challenges. Boards and senior management may anchor on the NOAA headline and question why the company's PML or aggregate limit has not decreased. The answer, which ERM actuaries need to articulate clearly, is that the PML reflects the long-term loss distribution and current exposure, not the expected frequency for a single season.

Reinsurance Purchasing Decisions

The below-normal forecast does influence one specific actuarial decision: the timing and aggressiveness of reinsurance purchasing. Cedents facing a buyer-favorable market with softening rates and a benign seasonal outlook may be tempted to reduce their catastrophe reinsurance purchases, accept higher retentions, or defer multi-year commitments in expectation of further softening. This is rational in a narrow sense; if the season is indeed quiet, the saved premium falls directly to the bottom line.

But the downside is asymmetric. A single major landfall in a high-exposure state could exhaust a reduced reinsurance tower and leave the cedent with net retention losses far exceeding the premium saved. The actuarial calculation, comparing the expected reinsurance cost savings against the tail risk of under-protected net positions, almost always favors maintaining or extending coverage when rates are falling. The cost of catastrophe protection is lowest precisely when the market perceives the risk as lowest, making it the optimal time to lock in coverage rather than reduce it.

The El Niño Variable: Real Signal, Limited Precision

The El Niño Southern Oscillation (ENSO) is the single most important predictor of Atlantic hurricane activity, and the 2026 forecast rests heavily on it. NOAA's Climate Prediction Center assigns an 82% probability to El Niño conditions developing between May and July 2026, with the signal expected to strengthen into the peak August-October hurricane window. CSU's forecast cites the potential for "robust El Niño" and "above-normal levels of vertical wind shear across the tropical Atlantic and Caribbean" as the primary drivers of their below-average call.

The physical mechanism is well understood. El Niño increases upper-level westerly winds across the Atlantic, creating vertical wind shear that disrupts the organized convection hurricanes need to form and intensify. The statistical relationship is robust: El Niño years average roughly 40% fewer named storms and 60% fewer major hurricanes than La Niña years across the post-1970 satellite era.

But the relationship is probabilistic, not deterministic. The 1992 season featured El Niño conditions and produced only 7 named storms, well below average. One of those storms was Hurricane Andrew, which struck Homestead, Florida as a Category 5 and generated $27 billion in insured losses (1992 dollars), triggering the insolvency of 11 Florida insurance companies. An El Niño year with few storms and one devastating landfall is not a theoretical scenario; it happened within living memory and reshaped the Florida property insurance market for a generation.

Additionally, ENSO's influence on hurricane formation is strongest in the Main Development Region (roughly 10-20°N latitude, 20-60°W longitude) and weakens for storms forming in the Gulf of Mexico or along the U.S. East Coast. Gulf of Mexico tropical cyclogenesis, which produces some of the shortest-lead-time landfall scenarios for Texas and Louisiana, is less sensitive to ENSO phase. A below-normal basin-wide count can coexist with an active Gulf season if local sea surface temperatures and low-level wind patterns favor development.

Market Dynamics: Record Capital Meets Forecast Optimism

The convergence of below-normal forecasts, record reinsurance capital ($785 billion globally), and double-digit rate declines creates a market environment where complacency risk is elevated. Howden Re characterized the January 2026 outcome as a market "re-balancing" rather than a return to prior soft-market underwriting practices, noting that "attachments remain elevated by historical standards, terms and conditions are tighter; capital is being deployed selectively." That distinction matters, but the directional trend is unmistakable.

The trajectory of rate declines is accelerating. Property-cat rates fell 8% at January 2025, then 14.7% at January 2026. Florida June 1 rates dropped 10% to 20% in 2026. ILS spreads have compressed as 60% of institutional investors surveyed by Gallagher Securities indicated plans to increase their ILS allocations. Cat bond issuance is running ahead of 2025's record pace, with $13.8 billion in bonds maturing and creating a reinvestment wave that further compresses pricing.

In this context, the below-normal NOAA forecast functions as a tailwind for further softening rather than as an independent driver. Reinsurers that were already conceding rate are under additional pressure to deploy capacity when the atmospheric conditions appear favorable. The risk is a self-reinforcing cycle: benign conditions attract capital, capital compresses pricing, compressed pricing encourages further capital deployment, and the market enters hurricane season with lower rates, higher capacity, and an expectation of low losses that may or may not materialize.

Why This Matters for Actuarial Practice

The disconnect between seasonal forecasts and cat models is not a theoretical curiosity. It has direct implications for how actuaries set rates, manage reserves, and communicate risk to stakeholders.

First, the cat load in rate filings should be driven by modeled losses, not seasonal expectations. ASOP No. 38 (Using Models Outside the Actuary's Area of Expertise) and ASOP No. 39 (Treatment of Catastrophe Losses in Property/Casualty Insurance Ratemaking) both require actuaries to understand the models they rely on, including their calibration methodology and time horizon. An actuary who reduces the cat load because the seasonal forecast is below-normal is making an adjustment that the model vendors themselves would not endorse.

Second, reinsurance purchasing decisions should not anchor on single-season forecasts. The optimal time to secure multi-year catastrophe coverage is when pricing is soft and seasonal expectations are benign. Actuaries advising on cession strategy should emphasize the long-term loss distribution and the asymmetric cost of being under-protected.

Third, reserve adequacy monitoring should remain independent of seasonal narratives. The temptation to release hurricane reserves during a quiet season or to reduce IBNR loads based on favorable forecasts has historically preceded adverse development in subsequent years. Catastrophe reserves should reflect the actuarial estimate of ultimate losses, not the atmospheric conditions of the current season.

Fourth, communicating with boards and senior management requires framing the seasonal forecast within the broader risk context. The NOAA outlook is a useful data point about expected basin-wide activity. It is not a risk assessment. The risk, measured by modeled PML and aggregate exceedance probability, reflects the exposure base, the vulnerability of the portfolio, and the full range of possible hurricane scenarios, not just the expected frequency for one season.