Vol. 129, No. 4 * Pages 373–484 * October - December 2025

The present study analyzes the long-term (1871–2020) precipitation time series of Mosonmagyaróvár (Hungary) and investigates the precipitation trends affecting winter wheat production. Understanding precipitation trends is important for agriculture due to the increasing frequency and intensity of droughts caused by climate change.
In this study, parametric and non-parametric trend tests (linear and Mann-Kendall trend test) were applied, which showed a significant decrease in April and October. A significant downward shift of the mean can be demonstrated in spring by Pettitt’s test. This decrease has a negative impact on key growing periods for winter wheat, which poses a serious challenge to conventional wheat production in the region. The research highlights the importance of different agrotechnical solutions to reduce yield losses due to climate change. The results obtained are in line with trends observed in Keszthely (Hungary), which confirms the regional changes.
These climatic changes can have a significant impact on the cultivation of our most important domestic food crop, winter wheat, so it is worth preparing for adaptation from this point of view as well.

An exact statistical description of present and future climate requires a database representative in space and time. However, observation records – that is raw climatological time series – are loaded with inhomogeneities due to changes in the location of the weather stations and usage of different instruments and observation protocols. Datasets must be homogenized first, which means that previous measurement data must be adjusted to the present observation protocols, while missing data must be supplemented. The data base of the present examination is the homogenized precipitation time series of Hungary, that is diurnal amounts of precipitation for the 1233 grid cells which cover the area of the country over the period of 1971-2022 in the state of the database in 2023. Firstly, the diurnal amount of precipitation over the area of the country, that is the sum of precipitation what falls in each cell of the grid over the area of the country has been chosen as a variable to be analyzed. Its annual and monthly characteristics have been analyzed for different independent variables. Secondly, spatial characteristics of the diurnal amount of precipitation, that is its distribution among the grid cells have been examined as well. In this article, after summarizing the climatic characteristics and the characteristics for the examined period of the total precipitation in Hungary, we analyze the spatial and temporal statistical properties of the daily dry grids and the dry days per grid. Dry days and grid cells are those when and where the daily precipitation amount is under 0.1 mm.

This study analyzes long-term (1940–2023) monthly temperature trends across the Northern Hemisphere, focusing on tropical, temperate, and polar regions, as well as key mountainous areas such as the Rocky Mountains, the Tibetan Plateau, and the Alps. Results show that polar regions experienced the highest seasonal temperature increase, averaging 0.081°C per season during winter, while tropical regions exhibited the lowest increase, with 0.036°C during winter. In temperate regions, seasonal warming trends ranged from 0.05°C in winter to 0.039°C in summer. Monthly trends revealed that February and March exhibited the highest increases, with rates of 0.0195 °C and 0.0194 °C, respectively, while August showed the lowest increase at 0.0116 °C. Furthermore, trend maps indicate that over 92% of the Northern Hemisphere experienced warming across all months except June and January. These findings provide a comprehensive understanding of regional and seasonal temperature variations in the Northern Hemisphere, emphasizing the importance of localized and temporal analyses for a more nuanced perspective on climate change.

To better understand outdoor thermal comfort on both seasonal and monthly levels, the current trends and anomalies in the Podrinje-Valjevo Region (PVR) over the past 30 years (1991–2020) and their impact on tourist activities, two bioclimatic indices, the Universal Thermal Climate Index (UTCI) and the Tourism Climatic Index (TCI), were utilized for the temporal assessment of bioclimatic conditions in Loznica and Valjevo. The results show that spring and autumn are the most favorable seasons for outdoor tourism activities, with April, May, September, and October being particularly optimal. According to UTCI, November has also become more bioclimatically favorable due to a rise in average monthly UTCI values. Additionally, UTCI data reveal a notable upward trend in seasonal anomalies, especially during autumn and spring, with average seasonal UTCI values increasing. Although TCI indicates that summer is particularly ideal for tourist outdoor activities and tourists’ thermal comfort, UTCI highlights that summer months can cause significant thermal discomfort due to moderate heat stress. The results obtained can serve to more effective tourism planning in the Podrinje-Valjevo Region in Serbia.

This study investigated the spatial and temporal distribution of aridity indices to determine climatic conditions in regions of southern and eastern Serbia in the period 1961-2022. We used the mean monthly and annual air temperature and precipitation data obtained from nine meteorological stations in the study area. Three indices were used to quantify aridity: the de Martonne aridity index, the Emberger index and the Pinna combinative index. Calculations are carried out on an annual scale for all the indices mentioned. The results show large territorial differences in the de Martonne aridity index, which distinguishes between two climate types on an annual basis. The Emberger index is characterized by humid climate types for all meteorological stations in the area. The Pinna combinative index also indicates humid conditions in the entire area, although the annual values of the index vary considerably. There was no change in the trend of aridity in the study area during the period of investigation. The spatial distribution was determined via the inverse distance weighting interpolated method. The Mann-Kendall test indicated that the aridity trends at all the meteorological stations were not statistically significant.