Risk & Catastrophe · Industry loss estimate

PERILS puts initial insured loss from August storm Raphaela at €3.3bn

PERILS has published an initial estimate of €3.296bn for insured property and motor losses from severe convective storm Raphaela, which hit France, Germany, Switzerland and Italy from 26 to 28 August 2026. The Swiss losses rank among that market's largest catastrophe losses on record, Reinsurance News reported, and PERILS will publish an updated estimate on 28 November.

Illustration of a lake valley under passing storm clouds, with hailstones lying on a roof and a car bonnet.
Illustration generated with AI for insurewire.org. Not a photograph of the event.

Insured property and motor losses from the severe convective storm (SCS) that PERILS calls Raphaela come to €3,296m on a first estimate, Reinsurance News reported on 19 October. PERILS based the figure on loss data from affected insurers and will revise it on 28 November 2026.

Switzerland is the outlier. PERILS ranks the Swiss share among the largest catastrophe losses recorded in that market, after a band of hail crossed the country between Lake Geneva and Lake Constance. Elsewhere the losses stayed below recent extremes, although the area around Besançon in eastern France suffered heavy damage.

The event covered three days, 26 to 28 August 2026, and moved from France into Switzerland, southern Germany and northern Italy. Hail of 4 to 8 cm fell in all four countries, and stones of up to 13 cm were reported in the Italian regions of Lombardy and Emilia-Romagna. Strong wind, heavy rain and flash floods added to the damage.

Luzi Hitz, product manager at PERILS, said Raphaela is the second European SCS event this year with industry losses above €2bn, after one in mid-July. He connected both events to the heatwaves before them and said multi-billion convective storms in Europe could become "the new normal".

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Reinsurance News ↗

News publisher · 19 Oct 2026

This is an original brief based on the linked publication. Company statements and forecasts are attributed to their source.

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