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.

Land surface models with detailed urban parameterization schemes provide adequate tools to estimate the impact of climate change in cities, because they rely on the results of the regional climate model, while operating on km scale at low cost. In this paper, the SURFEX land surface model driven by the evaluation and control runs of ALADIN-Climate regional climate model is validated over Budapest from the aspect of urban impact on temperature. First, surface temperature of SURFEX with forcings from ERA-Interim driven ALADIN-Climate was compared against the MODIS land surface temperature for a 3-year period. Second, the impact of the ARPEGE global climate model driven ALADIN-Climate was assessed on the 2 m temperature of SURFEX and was validated against measurements of a suburban station for 30 years. The spatial extent of surface urban heat island (SUHI) is exaggerated in SURFEX from spring to autumn, because the urbanized gridcells are generally warmer than their rural vicinity, while the observed SUHI extent is more variable. The model reasonably simulates the seasonal means and diurnal cycle of the 2 m temperature in the suburban gridpoint, except summer when strong positive bias occurs. However, comparing the two experiments from the aspect of nocturnal UHI, only minor differences arose. The thorough validation underpins the applicability of SURFEX driven by ALADIN-Climate for future urban climate projections.

There is a growing need to develop climate services both at national and international level, to bridge the gap between the providers and the end-users of climate information. Several national climate services are aiming to serve the local users’ needs by creating web portals. Thanks to this trend, the number of available climate data (both measured and modelled) is rapidly growing and often there is not any personal contact between the users and the climate scientists via the web portals. Therefore, it is important to make this service usable and informative and train the potential users about the nature, strengths and limits of climate data.Within the framework of a national funded project (KlimAdat), the regional climate model projections of the Hungarian Meteorological Service are extended and a representative climate database is developed. Regular workshops are organised, where we get hands-on information about the requirements and give training about climate modelling in exchange. One of the most discussed issue during the workshops is tackling with uncertainty information of climate projections in climate change adaptation studies. The future changes are quantified in probabilistic form, applying ensemble technique, i.e. several climate model simulations prepared with different global and regional climate models and anthropogenic scenarios are evaluated simultaneously.In order to help the users orienting through the mushrooming climate projections, a user guide is prepared. Topics are e.g. how to select model simulations, how to take into account model validation results and what is the difference between signal and noise. The guideline is based on 24 simulations of the 12-km resolution Euro-CORDEX regional climate models, driven by the RCP4.5 and RCP8.5 scenarios. Two target groups are distinguished based on the required level of post-processing climate data: 1) climate impact modellers, who need large amount of raw or bias corrected data to drive their own impact model; 2) decision makers and planners, who need heavily processed but lightweight data. The purpose of our guideline is to provide insight into the customized methodologies used at the Hungarian Meteorological Service for fulfilling users’ needs.