A CLIMATE VIEW OF RISK

Does your tropical cyclone cat model know it's 2026?

You know the climate that produced the historical record is not the climate producing today's storms. The question is whether your model captures that change in a way you can defend.

Trusted by the world's top risk professionals

THE CHALLENGE

Themodelisdoingexactlywhatitwasdesignedtodo.That'stheproblem.

Each year, the same question comes up: is risk aligned with the losses that could occur? Whether you sit in underwriting, analytics, capital or risk, your best estimates of catastrophe loss underpin the core of the business.

When the questions come: "are those estimates based on the climate we’re in today, or the one reflected in the historical record?" how comfortable are you with your answer?

You sit down with the client to walk them through their parametric cover. The conversation goes the way it always does. The client follows your argument. They nod agreeably.

Two weeks later, the email comes. Polite. They've increased their traditional limit at renewal. They won't need a parametric layer this year.

Not a rejection. Another exit. The hit rate is brutal.

HOW WE GOT HERE

Your model was built for a climate that no longer exists.

Legacy cat models resample history and project the average forward. In a stable climate, that approach works. But the climate has changed. The gap between what your model says and what you're experiencing was built in from the start.

First generation: Statistical

Historical storms are resampled to create alternative scenarios. Outcomes stay within the range of what has already happened.

Second generation: Beyond history

Models expand that range using better tracks and higher resolution. This has become the industry standard. But the underlying assumption of a stable climate baseline remains.

Third generation: Climate-conditioned

Current climate conditions such as ENSO cycles, AMO, SST patterns, and steering flow all influence storm behaviour. A third-generation model can represent those conditions directly and show how they shape risk.

So, you adjusted for it, but none of it holds up

You knew your second generation model needed adjusting. So you increased hurricane frequencies by, say, 20%. Why 20%? Because adjusting down didn't feel right. Because you didn't want a step change. Because it felt conservative enough to defend.

But Florida and the Gulf Coast aren't experiencing the same climate response as the Northeast. A uniform frequency adjustment treats them identically.

After the next active season, someone will ask what that adjustment actually accounted for. You won't have a clean answer.

Adjustments help, but they’re hard to justify

Its common practice to apply adjustments to second generation models such as increasing event frequency by a fixed percentage.

But climate signals vary by region, and a single adjustment applies the same change everywhere. Over time, it becomes difficult to explain what that adjustment represents or how it should evolve.

Solutions

Use cases

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THE SOLUTION

The next generation of cat model. One that knows the physics.

Reask's Unified Tropical Cyclone (UTC) model simulates millions of physically realistic storms from the conditions that drive them today: sea surface temperatures, wind shear, mean sea level pressure, and steering patterns. The result is a view of risk conditioned on the current climate, not the average of the last 40 years.

DEEPCYC TRACKS

Over 2M years of simulated stochastic tracks, conditioned on the climate

DeepCyc generates physically realistic tropical cyclone tracks across all basins, giving you a globally consistent, climate-aware view of tropical cyclone risk for portfolio-level analysis and pricing.

Global coverage across all tropical cyclone basins

Climate-connected tracks continuously spanning past, present, seasonal, and future conditions (1.5°C to 3°C+)

Quantify shifts in risk in a changing climate

Explore DeepCyc

LiveCyc Scenarios

Over 2M years of simulated stochastic tracks, conditioned on the climate

Global coverage across all tropical cyclone basins

Climate-connected tracks continuously spanning past, present, seasonal, and future conditions (1.5°C to 3°C+)

Quantify shifts in risk in a changing climate

Explore DeepCyc

DEEPCYC TRACKS

Over 2M years of simulated stochastic tracks, conditioned on the climate

Global coverage across all tropical cyclone basins

Climate-connected tracks continuously spanning past, present, seasonal, and future conditions (1.5°C to 3°C+)

Quantify shifts in risk in a changing climate

Explore DeepCyc

BUILT TO INTEGRATE

The adjustment your model needs, without the rebuild

Reask's Climate-Based Risk Adjustment tool is a Python library that adjusts your existing cat model output to make risk distributions climate-aware. It works directly with standard loss tables, so teams can update their view of risk on top of the infrastructure they already have.

Works with standard cat model outputs and loss tables

One consistent framework across all basins with explicit correlation

Supports views from seasonal conditions through to long-term climate scenarios

Request a demo

BUILT TO INTEGRATE

The adjustment your model needs, without the rebuild

Integrates with all standard cat model outputs

One consistent framework across all basins with explicit correlation

Updated as conditions change, not locked to a static historical window

Request a demo

BUILT TO INTEGRATE

The adjustment your model needs, without the rebuild

Works with standard cat model outputs and loss tables

One consistent framework across all basins with explicit correlation

Supports views from seasonal conditions through to long-term climate scenarios

Request a demo

What a defensible risk view actually looks like

Peer-reviewed & benchmarked

Built on globally trusted climate data

One answer across every time horizon

Fits your existing workflow

How we decide what goes into our models

Track the latest academic research

Continuously monitor and contribute to emerging science to identify candidate processes that may drive tropical cyclone behaviour — and that are not yet represented in models.

Design data features to represent the process

Translate physical hypotheses into quantifiable data features — the observable proxies that could carry a signal if the process is real.

Let ML confirm or reject the signal

Apply machine learning to test whether the feature carries a statistically meaningful, data-emergent signal.

How we decide what goes into our models

Track the latest academic research

Continuously monitor and contribute to emerging science to identify candidate processes that may drive tropical cyclone behaviour — and that are not yet represented in models.

Design data features to represent the process

Translate physical hypotheses into quantifiable data features — the observable proxies that could carry a signal if the process is real.

Let ML confirm or reject the signal

Apply machine learning to test whether the feature carries a statistically meaningful, data-emergent signal.

How we decide what goes into our models

Track the latest academic research

Continuously monitor and contribute to emerging science to identify candidate processes that may drive tropical cyclone behaviour, including signals not yet represented in models.

Design data features to represent the process

Translate physical hypotheses into quantifiable data features — the observable proxies that could carry a signal if the process is real.

Let ML test the signal

Apply machine learning to test whether the feature carries a statistically meaningful, data-emergent signal. Features that pass the test are built into the model. Those that don't are rejected or refined.

How risk leaders are using our climate-conditioned cat models

SCENARIO PLANNING
MS Amlin: modeling hurricane losses under +2°C
RESEARCH PARTNERSHIP
With LGT ILS Partners: 40 years of climate-conditioned returns
SEASONAL OUTLOOK
Twelve Securis: framing the 2025 hurricane season with climate data
RISK QUANTIFICATION
Inigo: quantifying 2025 risk through Atlantic SST anomalies
ILS PORTFOLIO STRATEGY
Schroders Capital: integrating climate risk into ILS strategy

SCENARIO PLANNING
MS Amlin: modeling hurricane losses under +2°C
RESEARCH PARTNERSHIP
With LGT ILS Partners: 40 years of climate-conditioned returns
SEASONAL OUTLOOK
Twelve Securis: framing the 2025 hurricane season with climate data
RISK QUANTIFICATION
Inigo: quantifying 2025 risk through Atlantic SST anomalies
ILS PORTFOLIO STRATEGY
Schroders Capital: integrating climate risk into ILS strategy

"Published climate literature gave us basin-wide projections, but we needed regional landfall granularity we could feed directly into our event set. Reask's data let us isolate how warming shifts hurricane risk across 12 US coastal segments. That level of resolution simply wasn't available from other sources."
Sam Phibbs
Head of Catastrophe Research, MS Amlin
"Working with the Reask team on this paper gave us a way to quantify something the ILS market has long debated: whether seasonal climate forecasts are actually actionable for portfolio decisions. The 40-year back-test suggests they are, and by a meaningful margin."
Francesco Comola
Head of Analytics, LGT ILS Partners

"Published climate literature gave us basin-wide projections, but we needed regional landfall granularity we could feed directly into our event set. Reask's data let us isolate how warming shifts hurricane risk across 12 US coastal segments. That level of resolution simply wasn't available from other sources."
Sam Phibbs
Head of Catastrophe Research, MS Amlin
"Working with the Reask team on this paper gave us a way to quantify something the ILS market has long debated: whether seasonal climate forecasts are actually actionable for portfolio decisions. The 40-year back-test suggests they are, and by a meaningful margin."
Francesco Comola
Head of Analytics, LGT ILS Partners

"Published climate literature gave us basin-wide projections, but we needed regional landfall granularity we could feed directly into our event set. Reask's data let us isolate how warming shifts hurricane risk across 12 US coastal segments. That level of resolution simply wasn't available from other sources."
Sam Phibbs
Head of Catastrophe Research, MS Amlin
"Working with the Reask team on this paper gave us a way to quantify something the ILS market has long debated: whether seasonal climate forecasts are actually actionable for portfolio decisions. The 40-year back-test suggests they are, and by a meaningful margin."
Francesco Comola
Head of Analytics, LGT ILS Partners