The impact of climate change on global natural catastrophe insured losses.

I was sent some modeling a few years ago by someone doing a loss ratio walk from one year to the next on a property cat book. They took the loss ratio for the previous year, adjusted for rate change, adjusted for inflation, and then they did something else which you don’t normally see, they added a 3% load to cover the effects of ‘climate change’. It must have sat lodged in the back of my brain somewhere, because when last year on a different project, I saw someone else had made a similar adjustment and this time applied 2%, the first time I'd seen it popped into my head and I thought hmm, that’s interesting, what would my pick be?

Before we build a model, let’s think what a sensible range would be? The number is almost definitely not 0, but is it 1%, 2%, 5%, 10%?

At 1%, using rule of 72, total insured losses will double solely due to climate change (on top of regular inflation) every 72 years.
At 2%, we’re looking at 36 years.
At 3%, 24 years.
At 5%, we’re looking at 14 years.
And at 10%, we’re looking at 7 years.

10% seems heavy to me, it would mean in the last 25 years or so, cat losses have roughly 10x-ed. (1.1^25 = 10.8).
Anything from 1%-5% seems to be a plausible range to me.

Let’s see if we can build a model on top of the same Swiss Re data [1] we analysed in the last post, link to that post is here:
​www.lewiswalsh.net/blog/swiss-re-and-a-300bn-loss

TL;DR, based on the below analysis, I could generate a plausible range of 3-5% additional loading being required in relation to climate change. 
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Source: A pretty gnarly photo of the bobcat fire in Sep 2020, CA wildfire is definitely a region and peril in which climate change appears to be having an effect. @Eddiem360  https://upload.wikimedia.org/wikipedia/commons/c/c1/Bobcat_Fire%2C_Los_Angeles%2C_San_Gabriel_Mountains.jpg

Building a model

By ‘climate change loading’, my working definition is the additional % trend factor that needs to be added on top of ‘standard’ property claims inflation to capture the deterioration in weather related losses in recent years.
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The starting point of the analysis is the same Swiss Re aggregate cat loss info, going back to 1970, which I used in the last post from their 01/2025 Sigma report. [1]

We are going to approach the problems in two complimentary ways.

Method 1

Method 1 takes the Swiss Re dataset, and looks at the difference between the increase in weather related losses and the increase in EQ/Tsunami losses, with the assumption that the difference between the two is primarily driven by climate change. That is, we will take EQ/Tsunami losses as our base line, and any increase in weather related losses on top of this we will assume are caused by climate change. We assume social inflation on EQ/Tsunami and weather related have been roughly equal so this should remove that as a possible cause, and the same with changes in insurance penetration over time.

Method 1 assumes: 

Weather inflation = EQ inflation + climate change inflation 

We can estimate the first two components, and the third will be the difference between the two
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Method 2

Method 2 uses just the weather data, but this time we derive the total inflation in the data, and then subtract out monetary inflation and asset build up, with the assumption that what remains is due to climate change. Other include effects not stripped out, such as social inflation, changes in insurance penetration, may skew this, but it’s very hard to come up with robust estimates of these effects in order to remove them, and my working assumption would be that climate change is the single biggest driver of the residual inflation.

Method 2 assumes: 

Weather inflation = CPI inflation + real gdp growth + climate change inflation 

We can estimate the first three components, which will give us a view on the climate change component.
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As a measure of monetary inflation, in line with Swiss Re, I have used US cpi because the single largest peril is US windstorm. For asset build up, I have used US real gdp growth, this feels a little weak, but is consistent with what I’ve seen from other models, and is hard to beat as an objective measure.

Below is a Jupyter notebook which we are going to use to build our two models, 

​Results
Summary table of results.

We see that using the variations of the two methods, but stripping out versions which appeared unstable to small changes, we've got a minimum of 3.3% and a maximum of 4.3%. I'd suspect we haven't fully captured the uncertainty in the estimate with our variations, so let's assign this method a range of about 3-5%.

So there we have it, our model is suggesting that in order to capture the effects of climate change, we should be adding something like an additional 3-5% trend to our historical losses. Slightly heavier than the 2-3% I've seen being used before, but not miles away.

Additional caveats/uncertainties (not already mentioned)​

• Just because this was the correct historic factor, does not mean it will continue to be the correct factor going forward
• Analysis is primarily driven by the effects of US windstorms, other climate change exposed perils may have a higher or lower loading than implied by this analysis
• Effects such as social inflation, or an increase in assets in exposed areas (e.g. east coast), relative to the rest of the US, and various other effects, may be skewing both methods
• I’ve combined all weather and non-weather perils together which may be masking changes within any given peril. May be worth trying to examine different weather perils separately, but then we end up with issues around having a sufficient amount of data to do something credible.

I used an LLM (Claude Sonnet 4.5) to edit and proof read this post, but all words were written by myself, and all ideas (good and bad) were my own.

[1] sigma 1/2025: Natural catastrophes: insured losses on trend to USD 145 billion in 2025 | Swiss Re