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Extreme climatic events can have profound effects on ecosystems. Climate change is causing an increase in the frequency and intensity of extreme events, which raises the probability that natural ecosystems will be exposed to subsequent or simultaneous extreme events. Exposure to multiple extreme events may involve very different abiotic stressors, which makes it hard to compare their consequences for more than a single or a few species, limiting our knowledge of community level effects. Here, we propose a novel approach to assess how different abiotic stressors influenced soil arthropod communities in a coastal ecosystem, using a species abundance time series of more than two years. We first correlated shifts in community composition with the climatic conditions preceding the sampling dates. Temperature was found to be the most important factor influencing community composition in both locations. Based on local meteorological data, we then defined five types of extreme events which occurred during our monitoring period: heat, cold, drought, heavy precipitation and high sea water levels. To compare the effect of different types of extreme climatic events on community composition, we calculated the multidimensional Hellinger distance between two subsequent sampling dates, which is a measure for the compositional dissimilarity between communities. Extreme events were expected to result in a larger change in community composition between sampling dates and thus a larger Hellinger distance. However, no significant difference in Hellinger distance was found for intervals with or without extreme events in three out of the four location-vegetation combinations. In the saline location with an open vegetation type we found that Hellinger’s distance was reduced when extreme events had occurred, which is discussed in the light of the buffering potential of different vegetation types. Our study illustrates the novel use of an established method from the community ecological toolbox to facilitate direct comparison of different types of extreme climatic events on community composition. We highly encourage other ecologists with long-term monitoring datasets to perform similar analyses and test the general applicability of this method.