Trends in extreme precipitation events (SW Hungary) based on a high-density monitoring network

Climate change is commonly associated with extreme weather phenomena. Extreme weather patterns may bring prolonged drought periods, more intense runoff and increased severity of floods. Rainfall distribution is extremely erratic both in space and time, particularly in areas of rugged topography and heterogeneous land use. Therefore, locating major rainfall events and predicting their hydrological consequences is challenging. Hence, our study aimed at exploring the spatial and temporal patterns of daily rainfall totals of R >= 20 mm, R >= 30 mm and R >= 40 mm (extreme precipitation events, EPE) in P & eacute;cs (SW Hungary) by a hydrometeorological network (PHN) of 10 weather stations and the gridded database of the Hungarian Meteorological Service (OMSZ). Our results revealed that (a) OMSZ datasets indicated increasing frequencies of EPEs for the period of 1971-2020 in P & eacute;cs, (b) the OMSZ dataset generally underestimated EPE frequencies, particularly for R >= 40 mm EPEs, for the period of 2013 to 2020, and (c) PHN indicated a slight orographic effect, demonstrating spatial differences of EPEs between the two datasets both annually and seasonally for 2013-2020. Our results pointed out the adequacy of interpolated datasets for mesoscale detection of EPE distribution. However, topographically representative monitoring networks provide more detailed microscale data for the hydrological management of urban areas. Data from dense rain-gauge networks may complement interpolated datasets, facilitating complex environmental management actions and precautionary measures, particularly during weather-related calamities.