Diurnal variability of spatial pattern of air temperature was studied in five cities in Central Europe: Bratislava (Slovakia), Brno (Czech Republic), Kraków (Poland), Szeged (Hungary) and Vienna (Austria), during one of the heat waves in 2015 (4-14 August), with the application of micro-climate model MUK-LIMO_3. 8th August was chosen to study in detail the urban heat load at 10.0 0, 16.0 0, 22.0 0 and 4.00 CEST. Local Climate Zones concept was used to supply data for the model and for the interpretation of the results obtained. Model outcomes were validated with measurement data from 86 points belonging to the networks which operate in the cities studied. The results obtained show that among urban LCZ, the highest heat load was observed for LCZ 2 and 3 from 16.00 to 4.00, while at 10.00 there is no such clear pattern. Unlike forested areas, open green areas can contribute to the generation of high air temperature: > 35 °C during day time and > 30 °C during night time. Important factors controlling the intra-zonal and inter-zonal variability of air temperature in particular LCZs are the local environmental conditions. During the day time, diversified relief in the area of the city and its vicinities generates higher heat load in the valleys' floors than in areas located above, both in rural and urban areas. The same landforms experience lower heat load during the night time due to air temperature inversions effect.

Urban areas are among those most endangered with the potential global climate changes. The studies concerning the impact of global changes on local climate of cities are of a high significance for the urban inhabitants' health and wellbeing. This paper is the final report of a project (Urban climate in Central European cities and global climate change) with the aim to raise the public awareness on those issues in five Central European cities: Szeged (Hungary), Brno (Czech Republic), Bratislava (Slovakia), Kraków (Poland) and Vienna (Austria). Within the project, complex data concerning local geomorphological features, land use and long-term climatological data were used to perform the climate modelling analyses using the model MUKLIMO_3 provided by the German Weather Service (DWD).

The paper analyzes equivalent data for a low density meteorological station network (spatially discontinuous data) and poor temporal homogeneity of thunderstorm observational data. Due to that, a Regional Climate Model (RegCM) dataset was tested. The Most Unstable Convective Available Potential Energy index value (MUCAPE) above the 200 J kg −1 threshold was selected as a predictor describing favorable conditions for the occurrence of thunderstorms. The quality of the dataset was examined through a comparison between model results and soundings from several aerological stations in Central Europe. Good, statistically significant (0.05 significance level) results were obtained through correlation analysis; the value of Pearson's correlation coefficient was above 0.8 in every single case. Then, using methods associated with gridded climatology, data series for 44 weather stations were derived and an analysis of correlation between RegCM modeled data and in situ thunderstorm observations was conducted with coefficients in the range of 0.75-0.90. The possibility of employing the dataset in thunderstorm climatology analysis was checked via a few examples by mapping monthly, seasonal, and annual means. Moreover, longterm variability and trend analysis along with modeled MUCAPE data were tested. As a result, the RegCM modeled MUCAPE gridded dataset was proposed as an easily available, suitable, and valuable predictor for thunderstorm climatology analysis and mapping. Finally, some limitations are discussed and recommendations for further improvements are given.