Published October 2025.

Summary

The IRIS model estimates that climate change uplifted the intensity of a typhoon of the type like “Ragasa” from a weak Category 3 to a strong Category 3 at landfall. A “Ragasa” type typhoon at landfall is about +49% more likely in the 2025 climate compared to a pre-industrial baseline. The eyewall maximum precipitation has increased by 13%. We also estimate that about a third (36%) of the damage in South China of a “Ragasa” type typhoon can be attributed to climate change compared to the pre-industrial baseline. In a future +2°C warmer world we estimate an additional +27% to the current damages.

Background

Typhoon Ragasa had a life-time minimum pressure of 910 hPa over the Pacific making it a Category 5 typhoon. It was the strongest typhoon in 2025 at the time. Ragasa was a Category 3 typhoon at landfall in South China on the 24 September 2025. The IRIS model (Sparks and Toumi, 2024) can be used to infer the additional strengthening of a “Ragasa” type storm that can be attributed to recent warming or more specifically to recent changes in potential intensity. We first need to consider the change in the thermodynamic environment. There has been a recent global warming of about 0.2°C/decade putting the 2025 global mean temperature close to about 1.3°C above pre-industrial temperature. ERA5 reanalysis is used to calculate monthly mean PI fields between 1980 and 2024. We consider global warming to manifest itself differently with latitude. We have low confidence in attributing regional or longitude trends to global or anthropogenic warming. The regional changes are more likely to be caused by decadal variations and less likely to be sustained or representative of global warming. 

To calculate the PI field in any given year we apply the corresponding monthly (September) global zonal mean trend to the 1980-2024 monthly mean PI field. In this way we can estimate the regressed anomalous PI field in any month since industrialisation and scale this by the simultaneous global mean surface temperature. This regressed value is not the actual PI now but that portion due to a linear change since 1980. The pre-industrial state is equivalent to the global mean surface temperature of the 1950s. The pre-industrial value of PI is determined by regressing ERA5 backwards in time. 

Figure 1. Zonal mean potential intensity, PI, difference for September between now (“2025”, +1.3°C) and pre-industrial.

Figure 1 shows the zonal mean difference in August PI between 2025 and the pre-industrial estimate. There are large trends in the tropics which decline towards the subtropics and then increase again at larger latitudes. This meridional structure is interesting and is different to the SST trends which tend to gradually increase from the tropics to higher latitude. The difference in pattern between PI and SST is caused by the important role of moisture trends. The PI difference between 2025 and pre-industrial is about +6 m/s in the Pacific.

(We can also scale the current PI trends to project the PI changes to a +2°C warmer world. This increases the PI by about a further 3 m/s from today.)

The frequency of landfall is the next consideration. The IRIS model does not change the number of events in the Pacific, only the initial life-time maximum intensity is modified by the PI. Figure 2 shows the events of the 10,000 year simulation of typhoons near South China (within 2 degrees of the actual landfall location). The tracks in IRIS are generated as deviations of the observed/historical “parent tracks”. This method does convert many historic “near misses” to then enter the region of interest.

Figure 2. Simulated and observed event locations in the vicinity (2 degree radius) of Ragasa landfall for wind speed attribution.

Maximum wind speed at landfall 

Figure 3 shows the model return period plot for Typhoon wind speeds in the region near Ragasa’s landfall (2 degree radius). Landfall observations in the region are sparse over the 45 years used to build the IRIS model. The 2 degree radius region gives a reasonable sample for validation. In the case of Ragasa, a Category 3 at landfall, we estimate that this type of event was about 49% more likely compared to pre-industrial times. The return period has decreased from 33 yrs to 17 yrs. For the same return period the current wind speed compared to pre-industrial events has increased by 3.3 m/s or 7%. This is equivalent to making this type of storm more intense from a weak to strong Category 3. In a +2°C degree warmer world, the landfall wind speed increases by a further +2.1 m/s compared to the current climate (+1.3°C).

Figure 3. Landfall wind speed vs return period for the vicinity (2 degree radius) of Ragasa landfall as calculated for the current climate “2025” (+1.3°C, orange line), pre-industrial (blue line) and +2°C climate (red line). FAR is the Fractional Attributable Risk (Equation 1) of landfall with a maximum wind speed of 54 m/s in the current climate. ΔVmax is the change in Vmax between pre-industrial and current climates.

In the climate change attribution literature the fractional attributable risk, FAR, is frequently used. FAR is here defined as 

 FAR = (Pnow - PPre-Ind) / Pnow     (1)

where Pnow and PPre-Ind are the probabilities of an event of the minimum intensity for the current (now) and pre-industrial (Pre-Ind) climate respectively. The FAR for “Ragasa ” type is 0.49. This means that 49% of the likelihood of this type of event can be attributed to climate change.

Maximum rain rate at landfall 

We also calculate the change of the typhoon eyewall rain rate. The observed value of 17.1 mm/h is calculated as the maximum azimuthal mean rain rate at landfall from IMERG. Figure 4 shows the model return period plot for the maximum azimuthal mean rain rate for typhoons making landfall within a 2-degree radius of the landfall location of Ragasa. The 2-degree radius region gives a reasonable sample size. The return period has decreased from 8.8 yr to 6.7 yr, making this type of rainfall event 33% more likely. At the present return period, the current maximum rain rate compared to pre-industrial events has increased by 1.9 mm/h or 13%. A quarter of the rain risk can be attributed to climate change (FAR=0.25). 

In a +2°C scenario, the return period of a Ragasa-type event decreases further to 5.5 yr, which is equivalent to a 61.0% increase in frequency relative to pre-industrial times. From another perspective, the maximum rain rate of a pre-industrial Ragasa will have increased by 3.7 mm/h, a 22% increase. 

Figure 4. Return period curves of maximum azimuthal mean rain rate (mm/h) for typhoons making landfall within a 2° radius of the landfall location of Ragasa in the current climate (+1.3 °C, orange line), the pre-industrial climate (blue line), and the +2°C climate (red line). ΔFreq denotes the change in occurrence frequency (%) of a Ragasa-type event. FAR is the Fractional Attributable Risk (Equation 1) for a Ragasa-type rain event.

Attributing Direct Economic Damage

We are using IRIS to make an estimate of direct economic damage (loss) on physical assets. We have combined IRIS wind fields with a single damage function (Eberenz et al. 2021) and 10km gridded exposure adjusted for population growth and inflation (Eberenz et al. 2020). For the damage function we apply a minimum wind threshold for damage of 25.7 m/s and a half-damage wind speed of 135 m/s. We create a loss against intensity curve by calculating the damage for many stochastic TCs following the track of Ragasa. We then apply the landfall wind speeds for pre-industrial and current (+1.3°C) conditions and for a +2°C world (compared to pre-industrial) to the loss curve to estimate the damages. Wind is used as a proxy for tropical cyclone hazard, the damage function and calibration indirectly account for sub-perils such as storm surges and rainfall.

Figure 5 shows the economic damages estimated with variations of intensity, size and and different landfall locations centred on the track. We find the wind speed increase of this type of storm makes substantially more damage. The pre-industrial climate scenario is a counterfactual with the current exposure and vulnerability. To communicate the effect of climate change on loss we propose a new variable: the fractional attributable loss, FAL. It is defined as

FAL = (Lnow-LPre-Ind)/Lnow
(2)


where L is the economic loss for the current (now) and pre-industrial climate (Pre-Ind).

For the median loss we estimate the FAL for “Ragasa” is 0.36 because of the climate change driven intensification. This means 36% of the economic damage can be attributed to climate change compared to the pre-industrial baseline. We made many sensitivity studies and concluded that we can be much more confident in the estimate of the fractionable attributable loss than the absolute loss estimate. The FAL estimate is stable with a range of 0.31-0.39 to even a factor of nearly 100 in the range (10-90%tile) of the losses (Fig 5.). The future additional loss in a +2°C degree warmer world is an additional +27% from the current damages causing a near doubling of damages compared to pre-industrial climate.

Figure 5. Economic loss (2025 B$US) along the track in South China including Hong Kong vs maximum wind speed at landfall. Dashed lines show the pre-industrial and current (+1.3°C) climates and + 2°C scenario. Change of wind speed is +3.3 m/s between pre-industrial and the current climate and a further +2.1 m/s from current to +2°C. FAL is the fractional attributable loss for the intensification due to climate change to date (Equation 2). Line shows the median and shading shows the 10%-90% percentile range.

Eberenz, S., Stocker, D., Röösli, T., and Bresch, D. N.: Asset exposure data for global physical risk assessment, Earth Syst. Sci. Data, 12, 817–833, https://doi.org/10.5194/essd-12-817-2020, 2020.

Eberenz, S., Lüthi, S., and Bresch, D. N.: Regional tropical cyclone impact functions for globally consistent risk assessments, Nat. Hazards Earth Syst. Sci., 21, 393–415, https://doi.org/10.5194/nhess-21-393-2021, 2021.

Sparks, N., Toumi, R. The Imperial College Storm Model (IRIS) Dataset. Sci Data 11, 424. https://doi.org/10.1038/s41597-024-03250-y, 2024.