Abstract. After an overview of existing methods, we present a novel method of "event-adjusted" evaluation of extremeness of weather and climate events. It is based on optimization of both the considered area and the time duration for every event. The method consists of three steps: (i) estimation of return periods of a representative variable at individual sites, performed separately for various time windows; (ii) spatial interpolation of the point return period data; and (iii) averaging of return period values from individual pixels and optimization of the considered area and the time window. The optimization is enabled by multiplication of the common logarithm of the geometric mean of return periods by the radius of a circle area equivalent to the considered area. The maximum product is referred to as the Weather Extremity Index (WEI). The method is demonstrated by two precipitation events that affected the Czech Republic in May and in August 2010. The WEI is generally applicable regardless of the studied phenomenon (heavy rains, heat waves, windstorms, etc.). This fact makes it possible to study both weather and climate extremes more precisely from the viewpoint of possible recent and future changes in their frequency, seasonal distribution, and circulation conditions accompanying them.

Abstract.After an overview of existing methods, we present a novel method of "event-adjusted" evaluation of extremeness of weather and climate events. It is based on optimization of both the considered area and the time duration for every event. The method consists of three steps: (i) estimation of return periods of a representative variable at individual sites, performed separately for various time windows; (ii) spatial interpolation of the point return period data; and (iii) searching the area and the time window in which the extremeness of the event was maximum. The extremeness is quantified as the common logarithm of the spatial geometric mean of the return periods multiplied by the radius of a circle of the same area as the one over which the geometric mean is taken. The maximum product is referred to as the weather extremity index (WEI). Two precipitation events, which affected the Czech Republic in May and in August 2010, were evaluated by the WEI for illustration. Validation of the method on sufficiently long data series is still needed. Moreover, the WEI is generally applicable regardless of the studied phenomenon (heavy rains, heat waves, windstorms, etc.). This makes it possible to study various weather and climate extremes from the viewpoint of possible recent and future changes in their frequency, seasonal distribution, and circulation conditions accompanying them.

Abstract. This paper presents three indices for evaluation of hydrometeorological extremes, considering them as areal precipitation events and trans-basin floods. In contrast to common precipitation indices, the weather extremity index (WEI) reflects not only the highest precipitation amounts at individual gauges but also the rarity of the amounts, the size of the affected area, and the duration of the event. Furthermore, the aspect of precipitation seasonality was considered when defining the weather abnormality index (WAI), which enables the detection of precipitation extremes throughout the year. The precipitation indices are complemented with the flood extremity index (FEI) employing peak discharge data. A unified design of the three indices, based on return periods of station data, enables one to compare easily interannual and seasonal distributions of precipitation extremes and large floods.The indices were employed in evaluation of 50 hydrometeorological extremes of each type (extreme precipitation events, seasonally abnormal precipitation events, and large floods) during the period 1961-2010 in the Czech Republic. A preliminary study of discrepancies among historic values of the indices indicated that variations in the frequency and/or magnitude of floods can generally be due not only to variations in the magnitude of precipitation events but also to variations in their seasonal distribution and other factors, primarily the antecedent saturation.

Abstract. Runoff data were used to better select historically significant precipitation events. The suggested criterion Q x expresses the increase of a stream runoff over up to four days in a row. Tests confirmed that Q x maxima correspond to maxima of areal precipitation in the respective catchment. Ten significant precipitation events in summer half-years from 1951 to 2002 were selected in 25 catchments each, and further studied in respect to spatial extent, simultaneous occurrence in various river basins, seasonal distribution, and temporal variability. Four regions were recognised within Central Europe that show related seasonality and si-