Modeling and forecasting damage from wind storms is a major issue for insurance companies. In this article, we focus on the sensitivity of estimations of return periods for extreme events with respect to modeling assumptions and the type of input data. Numerous variables play a role: the quality of data concerning the location of insured buildings and weather report homogeneity, missing updates for correcting non-stationarities concerning the insurance portfolio history, ground roughness or climate change, the evolution of the model after an unprecedented event such as the Lothar storm observed in 1999 in Europe, temporal aggregation of daily events over several days, where events could span over several days up to one week, and storm trajectories, which could change due to global warming or sweep larger areas. Our work explores three important aspects. First, we highlight the geographic heterogeneity of the spatial distribution of wind speeds and the resulting damages. Therefore, we propose to partition the French territory into 6 relatively homogeneous storm zones, based on the dependence among observed wind speeds and geographic distance. Second, we extend a storm index—defined in Mornet et al. (Risk Anal 35:2029–2056, 2015)—to take into account geographic heterogeneity, and we analyze its tail behavior to show the difficulties met to obtain reliable results on extreme events. Third, we explore the calculation of Solvency Capital Requirements based on a model that we propose for the annual claim amount distribution. The purpose of our analysis is to quantify and to point out the high level of uncertainty in the computation of return periods and of other quantities strongly influenced by extreme events.