General awareness and overall interest regarding hailstorms and hail properties in Europe have increased significantly in the last several decades and have resulted in numerous local, national, and even Europe‐wide studies on hail and hail properties. To contribute to this field, we determined the hail climatology in the northeastern (NE) Adriatic region and analyzed its spatial and temporal patterns and performed an objectively derived weather type analysis of ERA5 daily mean data and instability indices. We studied the NE Adriatic region due to its focus on agricultural activities and on quality wine production. Our results are based on approximately 60 years of high spatial resolution measurements collected from 27 stations across complex terrain. The results show (i) high levels of spatial variability, (ii) significant annual variations, and (iii) hail throughout the whole year that (iv) intensifies in summer months. Furthermore, redistribution of hail among seasons (in particular, from summer to spring) was detected. Most significant changes were visible in the June–October period, with a negative trend of −0.06 hail days/year, and the period from November to March exhibited a positive trend of 0.13 hail cases/year. We found that deep cyclonic systems in front of and above our domain were most responsible for hail generation, often supported by southwesterly winds. Additionally, the vast majority of observed hail events occurred in unstable and sheared environments.

In this study, an investigation of a new thunderstorm intensity index (TSII) derived from lightning data is performed, along with its relationship to rain, wind, hail and waterspouts as well as instability indices (CAPE, LI, KI, and DLS). The study area is located in the northeastern Adriatic and includes various terrain types in a relatively small area (coastal, flatlands, hills and valleys, and mountain regions). The investigated period covers 11 years (2008–2018). The mathematical algorithm standing behind the TSII is based on the well-established methodology of lightning jump, allowing us to recognize areas where intensification in thunderstorms occurred. Our results suggest that these areas (with a positive TSII) experience significantly higher rain intensities and have higher total amounts of precipitation compared with areas where thunderstorms did not generate a TSII. Moreover, 76% of thunderstorm hail cases were associated with the presence of a TSII within a 15 km distance. The maximum reported wind speed also has higher values on a day with a TSII. Out of 27 waterspout events associated with lightning, 77% were related to a TSII. Due to the good spatial (3 km × 3 km) and high temporal (2 min) resolution of lightning data, the TSII can recognize even a local and short-lived intense system that is often misread by radars and satellites due to their inferior temporal resolution. The TSII is designed to be used as a climatological and diagnostic variable that could serve in lieu of more established data sources (e.g., station measurements and observations, radar imagery, etc.) if they are unavailable.

Climatic factors significantly influence travellers’ destination selection and demand for tourism products. To maintain sustainable tourism development, it is essential to monitor how tourist destinations will be affected by the projected future changes in the climate system. This study aims to reveal the future impact of climate change for tourism sector in Hungary using the Holiday Climate Index specified for urban tourism (HCI-Urban) and a modified version of Tourism Climate Index (mTCI). The paper employs an ensemble of multiple climate models (ALADIN5.2 and REMO2015) and emission scenarios (RCP4.5 and RCP8.5) to determine the future evolution of indices. Initially, the reference climatic conditions derived from the observational dataset CarpatClim-HU are presented. Then, the climate model results are validated against the observational database for both indices. Afterwards, bias-corrected model outputs are analysed for the periods 2041–2070 and 2071–2100 based on the ensemble of models and scenarios. The spatial distributions of indices are depicted on a monthly level and at a Hungarian district spatial scale. Both the observational and model results highlight that the climatic conditions are more favourable in spring and autumn compared to the summer months. It is also demonstrated that the climatic conditions in Hungary are expected to be more favourable or unchanged from autumn until spring, while there is a general worsening in summer. In terms of the future directions, larger uncertainties are found in May and September. Based on the anticipated trends, the paper pinpoints sector-specific recommendations for adapting tourism services to the altered climatic conditions.

In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.

The Hungarian Meteorological Service has been conducting climate model simulations in order to assess the effects of climate change in the Carpathian Basin and provide data for impact research and stakeholders. Two regional climate models are used: ALADIN-Climate 5.2 (hereafter ALADIN5.2) and REMO2015. They were tested for the past when the lateral boundary conditions were taken from two sources. ERA-Interim reanalysis was used in the evaluation experiment, while the CNRM-CM5 and the MPI-ESM-LR global climate models (GCM) provided the forcings in the control experiments. The model outputs were compared with the CarpatClim-HU observational dataset for the 1981–2000 period. Future projections were carried out with the RCP4.5 and RCP8.5 scenarios, and the results were analyzed for 2021–2050 and 2071–2100. The evaluation of the results focused mainly on climate indices calculated from temperature and precipitation. The validation results showed that REMO2015 assessed the mean temperature well, but the indices based on the minimum and maximum temperature had a significant bias which has to be taken into account when interpreting future changes. The model overestimated the minimum temperature in summer, which might affect the number of tropical nights. Moreover, the maximum temperature was underestimated; thus, the derived indices, such as the occurrence of summer days and hot days, were profoundly underestimated. In comparison, ALADIN5.2 had smaller biases for the high temperature indices; moreover, the number of hot days and extremely cold days was overestimated. Taking future projections into account, we can clearly see that the results of REMO2015 show a much more moderate increase in temperature than ALADIN5.2. The reasons are yet unknown and require further investigation. In spring and summer, the number of wet days was slightly overestimated, while the number of heavy precipitation days was marginally underestimated. The projections showed the highest uncertainty in the changes in mean summer precipitation and other precipitation indices. Although the REMO2015 model assessed a decrease in precipitation, ALADIN5.2 projected an increase in precipitation with a similar magnitude.

Lightning data provide very high spatial and temporal resolution allowing us to decompose thunderstorms into smaller segments. By using those segments we introduce a new Thunderstorm Intensity Index (TSII). Based on the mathematical background of lightning jump, TSII aims to identify the area which is most affected by the storm. Such index captures location in space and time where a thunderstorm experienced a sudden positive change in lightning activity, using the Eulerian standpoint. The advantage is independence to total number of flashes produced by the storm (which can vary significantly), and high temporal monitoring (2 min). An ongoing research (within SWALDRIC project) is performed on period of 11 years of lightning data and in a study area of NE Adriatic region. Validation is done against precipitation, wind, hail, waterspouts and comparison with ERA5 instability indices is made. Results show very good agreement between higher rain intensities and total precipitation in vicinity of TSII. Good agreement with hail occurrence, waterspout presence and wind gusts within 15km radius. Also, TSII turned to be invariant to the size of the system, thus allowing us to recognise small scale intense thunderstorms.