Published October 2026.
Direct Answer: Severe convective storm, flood, and wildfire often dominate modeled and actual property loss outside hurricane and earthquake zones. Models treat them as high-frequency, dispersed perils with attritional tails—not single peak events—so annual averages, protection gaps, and deductibles must be read against the peril that actually drives loss.
Your renewal pack still leads with hurricane PML and earthquake scenario loss, but the lines that move are hail, convective wind, pluvial and fluvial flood, and wildfire—including smoke and ember that never match your wildfire sublimit wording. For multi-state portfolios, that is a coverage design problem. Below: why primary-peril framing undercounted these drivers, how models treat them, and how to test deductibles and sublimits against protection-gap arithmetic.
Model releases, 2024–2025 (not a ranking)
- May 28, 2025: Moody’s released RiskLink / RiskBrowser Version 25, including North Atlantic Hurricane Models Version 25. Florida accepted Version 25 on June 12, 2025, valid until November 1, 2027.
- June 12, 2025 and June 19, 2025: Florida accepted Verisk Hurricane Model for the United States Version 3.0.0 on Touchstone 2024A and Touchstone 2025.
- July 24, 2025: California Department of Insurance: first completed wildfire-model review is the Verisk Wildfire Model. CDI was still reviewing Karen Clark & Company and Moody’s RMS.
- October 2, 2024: Moody’s North America Wildfire HD Version 2.0.
- June 2025: Verisk Touchstone / Touchstone Re 2025 (13.0): U.S. severe thunderstorm update, and inland flood for the UK and Republic of Ireland.
- December 10, 2025: Moody’s North America severe convective storm HD models. Loss figures in that launch are Moody’s claims, not an industry audit.
Why “Secondary” Stopped Meaning Small
A secondary peril is any cat peril that is not the account’s designated primary driver—usually hurricane or earthquake. SCS, inland flood, and wildfire stay labeled secondary on schedules even when they produce the largest single-year loss. Legacy benchmarks optimized for low-frequency, high-severity peaks; SCS and flood produce many moderate events nationwide, and wildfire concentrates in the wildland–urban interface. Over a decade those shapes often dominate expected loss versus a primary tail that never realized.
Reports still center wind tiers and quake zones while hail swaths sit in appendices. If secondary-peril contribution is material but the tower prices a 1-in-250 hurricane, layers are misaligned. The catastrophe modeling complete guide for 2026 covers the full stack; this piece stays on SCS, flood, and wildfire loss and program structure.
Severe Convective Storm: Frequency Over Peak
SCS bundles hail, tornado, straight-line wind, and related precipitation. Models simulate large catalogs of smaller footprints, not one mega-storm. SCS average annual loss scales with roof area, age, material, and spread—not a single landfall parameter.
Worked example: hail as an attritional layer
Take a $500M TIV schedule in central and southern states with aged built-up roofs. Modeled SCS AAL 0.12% of TIV ($600,000) before deductibles:
0.0012 × $500,000,000 = $600,000 modeled SCS AAL.
With a 2% per-occurrence deductible, only loss above that layer nets to you; gross SCS AAL still drives roof inventory and ACV questions. One regional outbreak can hit hundreds of locations—attritional in claims, fully represented in deep SCS catalogs.
Why did SCS losses explode in industry discourse? Exposure in hail-prone metros, larger locations, and higher values per square foot raise ground-up loss without any storm-climate change. Expanded SCS catalogs and finer hail resolution mean current runs attribute more loss than older versions on the same footprint. When comparing runs, ask whether the delta is catalog, vulnerability, or TIV—not only weather narrative.
Flood: Multiple Hydrologies, One Policy Gap
Flood is several hydrologies: fluvial, pluvial, surge, and special scenarios in different modules. Secondary-peril flood here means inland rain and river loss outside a named hurricane—often excluded or sublimited on property forms.
Protection-gap arithmetic
Ground-up flood versus insured flood after terms defines the protection gap. On a $200M campus, modeled 100-year ground-up flood might be $18M while property terms assume $0 insured flood without a private limit or NFIP.
Economic flood can read $18M while insured PML is zero under exclusions. Add a $5M flood sublimit: insured loss = min(ground-up, sublimit) after deductible, so $18M ground-up caps at $5M insured and $13M protection gap. Reproduce that min() by location when quoting facultative or parametric cover.
Flood is path-dependent on rainfall, soil moisture, drainage, and pad elevation—not zip code alone. Uncertainty is wide at address level; neighbors can diverge by feet of elevation. Blanket sublimits without geospatial review misstate insured loss. For wildfire alongside hurricane and earthquake context, see hurricane, earthquake, and wildfire catastrophe peril analysis; flood still demands hydrologic resolution.
Wildfire: Flame, Ember, and Smoke as Separate Mechanisms
Wildfire modules separate flame, ember ignition, and smoke where architecture allows. Flame hugs the perimeter; ember extends downwind; smoke can drive BI far from the burn. Policy wildfire sublimits may not match how claims code smoke versus fire—align model peril buckets to endorsements before comparing tails.
Concentration versus spread
Take $300M TIV across 50 WUI locations: wildfire AAL 0.08% ($240,000) but a 250-year ground-up cluster scenario of $45M. A $10M sublimit caps insured tail at $10M; $35M stays gap. Earthquake deductibles rarely trigger on ember; wind deductibles may not apply when loss codes as fire.
Models ingest fuel, slope, access, and mitigation credits where available. Ember spotting beyond the flame front drives divergent loss on opposite sides of a ridge. Treat year-over-year wildfire delta as exposure in the WUI plus catalog change, not automatic retention movement.
How Secondary-Peril Modeling Differs From Primary-Peril Modeling
Primary engines stress sparse tail events; secondary modules stress catalog breadth and seasonality. SCS curves look smoother below 100-year returns; insured flood can sit at zero until sublimits bind; wildfire tails stair-step by cluster. Use AAL for SCS negotiations; pair flood and wildfire AAL with tail scenarios at sublimit kneepoints.
Data prep differs: SCS needs roof and geocode precision; flood needs elevation and hydrology; wildfire needs fuel proximity. A few hundred feet of geocode error can flip flood or ember loss. Secondary perils punish address hygiene more than coarse quake zones.
Platform choice affects peril splits—see catastrophe modeling across RMS, AIR, and Verisk for context—but read your own export first. Fee allocation for extra layers is in catastrophe modeling costs and who pays; misaligned sublimits usually cost more than the run.
From Model Output to Deductibles and Sublimits
Secondary perils stack peril-specific triggers. On $400M TIV, suppose 2% all-risk deductible, $15M flood sublimit, $10M wildfire sublimit west, and full limits for SCS.
Illustrative hail: $22M ground-up SCS; 2% on $180M affected TIV = $3.6M deductible; ≈$18.4M net if limits apply. Pluvial flood $12M ground-up walks another path through flood sublimit and water deductibles. Each secondary peril uses different triggers in the same tower.
Hail is not windstorm on every form; flood sublimits without geospatial matching cap the wrong sites; wildfire sublimits may exclude smoke while BI pays. Treaties may treat SCS as attritional unless a complex threshold is met—AAL can sit below cat reinstatement while aggregates erode retention. See reinsurance treaty structures including quota share and excess-of-loss aggregate for how aggregates absorb many SCS years versus one wildfire tail.
Portfolio View: Accumulation Without a Landfall
Hail swaths, river basins, and fire complexes create accumulation without a landfall cone. Portfolio reviews that only cluster hurricanes miss SCS within hail swaths and flood by watershed. Tie secondary-peril aggregate metrics to reinsurance, not only named-storm PML—see catastrophe portfolio management for accumulation and PML.
Model-update cycles refresh secondary-peril catalogs often; see climate risk pricing and catastrophe model updates in 2026 for how that feeds pricing dialogue. Use updates to re-test sublimits, not to auto-raise retention.
What to Do Before Renewal
Request peril-disaggregated SCS, flood, and wildfire output with policy terms in the engine: ground-up and insured AAL plus one tail metric each. QA geocode and elevation on top locations per peril. Map deductibles and sublimits to model peril codes. Recompute protection gap as ground-up minus insured at the same return period. Secondary in name only, these perils are often primary in frequency and expected loss—align limits to the peril that sends the check.
FAQ
What counts as a secondary peril in catastrophe modeling?
In catastrophe modeling, a secondary peril is any modeled peril that is not the portfolio’s designated primary peril for rating and reporting—most often hurricane or earthquake. Severe convective storm, inland flood, and wildfire are routinely labeled secondary even when they drive the largest insured or economic losses in a given year. The label describes convention and report layout, not low importance. Models still simulate secondary perils with full catalogs, vulnerability functions, and financial modules. Your job is to read their metrics on the same footing as primary peaks when they affect limits and retentions.
Why did severe convective storm losses explode?
SCS losses grew because exposure increased in hail and convective-wind regions, values per square foot rose, and roof inventories aged into higher vulnerability bands. Model catalogs and hail-size resolution also improved, so current runs attribute more loss to SCS than older platform versions for the same footprint. Large regional outbreaks produce many simultaneous location claims that aggregate above retentions. That pattern is distinct from a single hurricane landfall but can exceed annual cat budgets. Separating exposure growth from catalog change keeps renewal debates honest.
How does secondary-peril modeling differ from primary-peril modeling?
Primary-peril modeling emphasizes rare, high-severity events with deep tail extrapolation for wind and quake. Secondary-peril modeling emphasizes many smaller to mid-size events, seasonal catalogs, and fine spatial footprints for hail, rain, and fire. Average annual loss is often more informative for SCS; flood and wildfire need tail review with sublimits applied. Secondary perils depend heavily on location attributes—roof, elevation, fuel—rather than coarse zones alone. Insured loss outputs require explicit policy terms because economic and insured curves diverge more than for all-risk wind on standard forms.
What makes flood so hard to model?
Flood loss hinges on local hydrology, elevation relative to drainage paths, rainfall intensity, and antecedent conditions—not on a single county flag. Pluvial flooding can occur far from mapped flood zones; fluvial flooding depends on upstream behavior. Small geocode or elevation errors can change loss dramatically. Policy exclusions and sublimits often zero out insured flood while economic loss remains, producing a wide protection gap. Models propagate uncertainty bands that brokers should not collapse to a single point without site review.
How do wildfire models treat ember versus flame damage?
Wildfire models typically simulate direct flame exposure at the fire front separately from ember ignition downwind and, where supported, smoke or ash impacts. Flame damage correlates with fuel continuity and structure spacing at the perimeter; ember damage extends into subdivisions that never see direct fire. Vulnerability curves differ by ignition pathway—ember losses often start at vulnerabilities such as vents, roofs, and decks. Policy language may not mirror these buckets, so align model peril codes with endorsements before comparing insured tail loss to sublimits.
What do secondary perils mean for my deductibles and sublimits?
Secondary perils mean deductibles and sublimits must be tested peril by peril, not only against hurricane or earthquake scenarios. SCS may erode retention through many mid-size hits; flood and wildfire sublimits cap tails below modeled ground-up loss and create protection gaps. Deductible wording for wind, water, and fire may not match how events are coded in claims or in the model. Reproduce min(limit, ground-up loss) after deductibles for each secondary peril at AAL and at a relevant tail. That arithmetic shows whether your tower matches the peril driving modeled loss.