This paper examines extreme precipitation events (EXPEs) and their trends based on daily precipitation values observed at 14 stations in Serbia for the period 1961–2020. The following EXPEs were investigated: RR10mm (heavy precipitation days), RR20mm (very heavy precipitation days), Rx1day (highest 1-day precipitation amount), Rx3day (highest 3-day precipitation amount), Rx5day (highest 5-day precipitation amount), R95p (very wet days) and R99p (extremely wet days). A positive trend for all EXPEs was dominant in Serbia from 1961 to 2020. All annual Rx1day time series show a positive trend, which is significant at 12 out of 14 stations. The highest values of all EXPEs were observed in 2014, when the annual precipitation totals were the highest at almost all stations in Serbia. To examine the potential influence of global warming, the mean values of the EXPEs were calculated for two periods: 1961–1990 and 1991–2020. In the second period, higher values were determined for all EXPEs than in the first period. The large-scale variability modes, such as the North Atlantic Oscillation (NAO), the East Atlantic Oscillation (EA), and the East Atlantic–West Russia (EAWR) pattern, were correlated with the EXPEs. A negative correlation was found between the EXPEs and the NAO and the EAWR, and a positive correlation between the EXPEs and the EA pattern. For future research, the contribution of high-resolution data will be examined.

We investigate multifractal properties of daily means of air temperature over the territory of Serbia, by using Multifractal detrended fluctuation analysis. Temperature anomalies in two periods 1961–1990 and 1991–2020 are calculated from the E-OBSv26.0e gridded dataset with 0.10° (~12 km) resolution, totaling 1278 daily temperature series for each period. The MFDFA parameters: position of the maximum of the spectrum α₀, width of the spectrum W and asymmetry r, obtained from the total of 2556 MFDFA runs are interpolated to yield their spatial distribution across Serbia in the two periods. We found several patterns in both the spatial distribution, and changes from first to second period. All series showed multifractal properties with overall persistent long-term correlations (α₀ > 0.5) and the dominance of small fluctuations (r > 0). The persistence is weaker (smaller α₀ values) and multifractality is stronger (larger width W) in southern mountainous regions. In the second period the values of α₀ increased indicating stronger persistence of temperature dynamics, while multifractality became stronger (larger W) in northern region and weaker (smaller W) in southern region. In both periods the contribution to multifractality was dominated by small fluctuations (r > 0) that become stronger in the second period, indicated by the increase of the values of r over most of the country’s area. These changes in the values of multifractal parameters indicate the increase of complexity of temperature dynamics in the second 30 years period which could be related to climate change.

In this study, extremely warm and cold temperature events were examined based on daily maximum (Tx) and minimum (Tn) temperatures observed at 11 stations in Serbia during the period 1949–2018. Summer days (SU), warm days (Tx90), and heat waves (HWs) were calculated based on daily maximum temperatures, while frost days (FD) and cold nights (Tn10) were derived from daily minimum temperatures. Absolute maximum and minimum temperatures in Serbia rose but were statistically significant only for Tx in winter. Positive trends of summer and warm days, and negative trends of frost days and cold nights were found. A high number of warm events (SU, Tx90, and HWs) were recorded over the last 20 years. Multiple linear regression (MLR) models were applied to find the relationship between extreme temperature events and atmospheric circulation. Typical atmospheric circulation patterns, previously determined for Serbia, were used as predictor variables. It was found that MLR models gave the best results for Tx90, FD, and Tn10 in winter.

In previous projects that focused on dynamical downscaling over Europe, e.g., PRUDENCE and ENSEMBLES, many regional climate models (RCMs) tended to overestimate summer air temperature and underestimate precipitation in this season in Southern and Southeastern Europe, leading to the so-called summer drying problem. This bias pattern occurred not only in the RCM results but also in the global climate model (GCM) results, so knowledge of the model uncertainties and their cascade is crucial for understanding and interpreting future climate. Our intention with this study was to examine whether a warm-and-dry bias is also present in the state-of-the-art EURO-CORDEX multi-model ensemble results in the summer season over the Pannonian Basin. Verification of EURO-CORDEX RCMs was carried out by using the E-OBS gridded dataset of daily mean, minimum, and maximum near-surface air temperature and total precipitation amount with a horizontal resolution of 0.1 degrees (approximately 12 km × 12 km) over the 1971–2000 time period. The model skill for selected period was expressed in terms of four verification scores: bias, centered root mean square error (RMSE), spatial correlation coefficient, and standard deviation. The main findings led us to conclude that most of the RCMs that overestimate temperature also underestimate precipitation. For some models, the positive temperature and negative precipitation bias were more emphasized, which led us to conclude that the problem was still present in most of the analyzed simulations.

A better understanding of climate change impact on dry/wet conditions is crucial for agricultural planning and the use of renewable energy, in terms of sustainable development and preservation of natural resources for future generations. The objective of this study was to investigate the impact of climate change on temporal fluctuations of dry/wet conditions in Serbia on multiple temporal scales through multifractal analysis of the standardized precipitation evapotranspiration index (SPEI). We used the well-known method of multifractal detrended fluctuation analysis (MFDFA), which is suitable for the analysis of scaling properties of nonstationary temporal series. The complexity of the underlying stochastic process was evaluated through the parameters of the multifractal spectrum: position of maximum ${\alpha }_0$ (persistence), spectrum width $W$ (degree of multifractality) and skew parameter $r$ dominance of large/small fluctuations). MFDFA was applied on SPEI time series for the accumulation time scale of 1, 3, 6 and 12 months that were calculated using the high-resolution meteorological gridded dataset E-OBS for the period from 1961 to 2020. The impact of climate change was investigated by comparing two standard climatic periods (1961–1990 and 1991–2020). We found that all the SPEI series show multifractal properties with the dominant contribution of small fluctuations. The short and medium dry/wet conditions described by SPEI-1, SPEI-3, and SPEI-6 are persistent ($0.5<{\alpha }_0<1$); stronger persistence is found at higher accumulation time scales, while the SPEI-12 time series is antipersistent ($0<{\alpha }_0-1<0.5$). The degree of multifractality increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. In the second period, the SPEI-1, SPEI-3, and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium dry/wet conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward a more random regime and stronger multifractality in the eastern and central parts of the country, indicating that long-term dry/wet conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction. These results indicate that the complexity of dry/wet conditions, in this case described by the multifractal properties of the SPEI temporal series, is affected by climate change.