Three different clothing thermal resistance (r cl) schemes are compared using meteorological and human data collected in Martonvásár. Model 1 is the most complex, it is energy balance based. Model 3 is the simplest, it is the UTCI-clothing model used as submodel in the UTCI (Universal Thermal Climate Index) scheme. It uses air temperature as sole input. Model 2 uses more data than model 3, the data used are the thermal insulation values of the clothing worn. Meteorological data refer to the town Martonvásár. The data were collected in the period August 9, 2016-May 23, 2018. The main result is that the r cl values obtained by model 1 differ significantly in most of the cases from the results obtained by models 3 or 2. The fact that the results of model 1 rarely match the results of model 3 or model 2 suggests that the energy balance between human body and environment is rarely achieved, merely this is the case in approximately 10 percent of the cases.

A new clothing thermal resistance scheme is presented and verified for the Carpathian region and for the time period 1971–2000. The scheme is as simple as possible by connecting operative temperature to air temperature, which allows for it to only use air temperature and wind speed data as meteorological inputs. Another strength of the scheme is that a walking person’s metabolic heat flux density is also simply simulated without having to regard any thermoregulation processes. Human thermal load in the above region is characterised by a representative adult Hungarian male and female with a body mass index of 23–27 kgm⁻². Our most important findings are as follows: (1) human thermal load in the Carpathian region is relief dependent; (2) the scheme cannot be applied in the lowland areas of the region in the month of July since the energy balance is not met; (3) in the same areas but during the course of the year, clothing thermal resistance values are between 0.4 and 1 clo; (4) clothing thermal resistance can reach 1–1.2 clo in the mountains in the month of July, but during the course of the year this value is 1.8 clo; and (5) the highest clothing thermal resistance values can be found in January reaching about 2.5 clo. The scheme may be easily applied to any another region by determining new, region-specific, operative temperature–air temperature relationships.

In this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well.

The length of the beach season at Lake Balaton (Hungary, Central Europe) in the period 2002–2024 is estimated using daily data of air temperature, water temperature and wind speed. Daily thermal load is approached by calculating operative temperature using operative temperature-air temperature relationships on monthly scale. We introduced the concepts of beach day and bath day in order to estimate when the environmental conditions were suitable for lakeside activities. Data are taken from meteorological station Siófok. The main results are as follows. In the summer months, the median number of beach days and bath days is 72 and 68, respectively. The unbiased standard deviation of beach days and bath days is 4.62 and 6.4 days, respectively. As regards bath days, the best year was 2023, then the number of bath days was 105. The smallest number of bath days was 57; this occurred in the year 2010. As regards beach days, the best year was 2018 with 114 days, the worst year was 2021 with 72 days.

We used a clothing thermal resistance model to investigate and compare the effects of climate and human variability on human thermal load. To investigate the effect of climate variability, we introduced the mean clothing thermal resistance $\overline{r_{cl}}$. For characterizing the effect of human variability, we used the standard deviation of clothing thermal resistance $∆r_{cl}$. We distinguished people based on their body type. We also defined the average human, a man and a woman, with thermal resistances of r_cl,m and r_cl,f. The investigation was carried out for the European region in the cold season for the period of 1981–2010. The climate variables were taken from the ERA5 reanalysis database. Our most important results are the following. (1) The macroscale pattern of the $\overline{r_{cl}}$ and $∆r_{cl}$ fields are very similar, based on which it can be stated that human variability does not modify the spatial distribution of $\overline{r_{cl}}$. (2) The $∆r_{cl}$ values are roughly a quarter of the $\overline{r_{cl}}$ values. The highest $\overline{r_{cl}}$ values (3.2–3.4 clo) are in Lapland, and the smallest (1–1.2 clo) in Andalusia. (3) The macroscale pattern of the r_cl,m and r_cl,f fields is similar to the macroscale pattern of the r_cl values of the mesomorphic person r_cl,2. The field of r_cl,2 can be used for climate classification purposes.