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In this paper, we implement the region-of- influence (ROI) approach for modelling probabilities of heavy 1-day and 5-day precipitation amounts in the Czech Republic. The pooling groups are constructed according to (i) the regional homogeneity criterion (assessed by a built-in regional homogeneity test), which requires that in a pooling group the distributions of extremes are identical after scaling by the at-site mean; and (ii) the 5T rule, which sets the min- imum number of stations to be included in a pooling group for estimation of a quantile corresponding to return period T. The similarity of sites is evaluated in terms of climato- logical and geographical site characteristics. We carry out a series of sensitivity analyses by means of Monte Carlo sim- ulations in order to explore the importance of the individual site attributes, including hybrid pooling schemes that com- bine both types of the site attributes with different relative weights. We conclude that in a dense network of precipitation stations in the Czech Republic (on average 1 station in a square of about 20◊20 km), the actual distance between the sites plays the most important role in determining the similarity of probability distributions of heavy precipitation. There are, however, differences between the optimum pool- ing schemes depending on the duration of the precipitation events. While in the case of 1-day precipitation amounts the pooling scheme based on the geographical proximity of sites outperforms all hybrid schemes, for multi-day amounts the inclusion of climatological site characteristics (although with much lower weights compared to the geographical distance) enhances the performance of the pooling schemes. This find- ing is in agreement with the climatological expectation since multi-day heavy precipitation events are more closely linked to some typical precipitation patterns over central Europe (re- lated e.g. to the varied roles of Atlantic and Mediterranean influences) while the dependence of 1-day extremes on cli- matological characteristics such as mean annual precipitation is much weaker. The findings of the paper show a promising perspective for an application of the ROI methodology in evaluating outputs of regional climate models with high resolution: the pool- ing schemes might serve for defining weighting functions, and the large spatial variability in the grid-box estimates of high quantiles of precipitation amounts may efficiently be re- duced.

Comparison of region-of-influence methods for estimating high quantiles of precipitation in a dense dataset in the Czech Republic