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The purpose of this study is to analyze the spatiotemporal variations in drought in Serbia over recent decades using the standardized precipitation evapotranspiration index (SPEI). The analysis is performed for time series of 1-, 3-, 6-, and 12-month SPEI indices (SPEI-01, SPEI-03, SPEI-06 and SPEI-12, respectively) calculated using the E -OBS gridded dataset with a horizontal resolution of 0.1° for the period of 1950–2022. In general, it was found that negative SPEI trends prevailed in Serbia for all months and all analyzed SPEI timescales, indicating a more frequent appearance of droughts in recent decades. Moreover, for a large majority of month-timescale combinations, the area with a negative trend was larger than the area with a positive trend. For several reasons, we focused on the SPEI for August. First, the SPEI for this month shows one of the most pronounced negative trends, which is rather consistent over different timescales and consistent in terms of the area over which the trend was found to be significant. Statistically significant negative trends of SPEI-03, −06 and − 12 for August were found over 11, 17 and 38% of the country area, respectively. To better understand the trends and changes in the SPEI for August, focusing on extreme and severe drought events, an innovative trend analysis (ITA) was applied. It was found that the occurrence of both extreme and severe SPEI drought categories has doubled over the last thirty years in comparison with the previous thirty-year period. Additionally, more frequent transitions from neutral and wet categories to dry categories were observed through an application of the Markov chain. Second, the special focus on SPEI-03, −06 and − 12 for August was motivated by the fact that we found a significant correlation between these index values and the annual maize production in Serbia, indicating that the SPEI for August can be useful for the assessment of drought impact on agriculture. To better understand interannual variability and long-term trends, this paper also explores the relations between the SPEI over Serbia and large-scale circulation patterns, together with general global and continental warming trends. Our findings indicate that drought changes over recent decades may be driven by the interplay of long-term warming and large-scale circulation patterns. In the context of climate change, especially climate projections, Serbia is situated in a region for which climate projections of mean precipitation change demonstrate weak signals and often with conflicting signs of change within multimodel ensembles. From this point of view, our results can be seen as a contribution to a better understanding of changes in extremes, in our case droughts, that can be hidden by inconclusive changes in the mean values. Finally, due to the similar situation in terms of precipitation and drought trends and changes, our results may be relevant for a wider region, and not only for Serbia. • For Serbia negative trends in SPEI index, for different months and timescales, are dominant. • Occurrence of extreme drought categories has doubled over the last decades. • Transitions from neutral and wet categories to dry categories are more frequent. • These changes are driven by long-term warming and large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Most studies on the relationship between global warming and El Niño-Southern Oscillation (ENSO) rely on simulation experiments, but they have produced contradictory results, which cannot guarantee the reliability of the experimental findings. To address this challenge, we approached the issue from the perspective of objective ENSO and global warming signals. The effects of global warming on ENSO are not limited to the Earth's surface, but also present in the atmosphere. While objective monitoring indices exist for surface global warming and ENSO signals, objective ENSO and global warming signals in the atmosphere are difficult to obtain. To obtain high-precision ENSO and global warming signals in the atmosphere, we propose using the strategy of ICA (Independent Component Analysis) combined with a non-parametric method. The results of the objective ENSO and global warming signals suggest that the global warming signal leads the ENSO signal by approximately three months and exhibits a clear positive correlation with the ENSO signal, especially the occurrence of strong ENSO events is closely related to global warming, indicating that global warming promotes the occurrence of ENSO events. Under the influence of global warming, the frequency and intensity of ENSO increase, the characteristics and complexity of ENSO atmospheric teleconnections may be altered, and there is a greater tendency for more destructive El Niño events to occur. Since the beginning of the 21st century, the probability of ENSO event has increased significantly. Furthermore, moderate or strong ENSO events tend to develop earlier, and extreme ENSO events not only develop earlier but also prolong their decay phase, leading to an extended lifespan of ENSO events. This paper investigates the effect of global warming on ENSO from the perspectives of objective ENSO and global warming signals, which well supplements the objective factual basis for the previous simulation experimental studies. • Most simulation studies on the relationship between global warming and ENSO cannot guarantee the authenticity ofresults • We investigated the relationship between global warming and ENSO from the perspective of objective signals. • To obtain ENSO and global warming signals, we proposed the strategy of ICA combined with a non-parametric method. • The results suggest that the global warming signal exhibits a clear positive correlation with the ENSO signal. • Under influence of global warming, frequency, lifespan, intensity and atmospheric teleconnections of ENSO may be altered [ABSTRACT FROM AUTHOR]

This paper investigates the performance of bias-corrected Flexible Global Ocean-Atmosphere-Land System-g3 (FGOALS-g3) model products and then detects changes in extreme precipitation (EP) in the Tienshan Mountains, Central Asia (TMCA), as reflected by 25 EP indices. The reliability of the FGOALS-g3 model outputs is assessed systematically based on multiple statistical indicators against multi-source precipitation datasets and the bias-corrected FGOALS-g3 model products are applied to project EP variations under different global warming levels. Using the geographical detector method, a novel statistical method for detecting spatial heterogeneity and elucidating the underlying causes, the explanatory power of 20 atmospheric circulation factors related to EP is examined. The findings indicate that while the FGOALS-g3 products can detect the spatial pattern of multi-year average precipitation in the TMCA, there is an obvious overestimation in magnitude, especially in the West and Middle Tienshan Mountains. These biases are significantly reduced, however, after downscaling and bias correction. Compared with the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, the grid-by-grid error in bias-corrected FGOALS-g3 simulated mean precipitation is between −7.64% and 10.95%. In terms of EP, the corrected FGOALS-g3 products not only reproduce the spatial distribution, but also reasonably simulate their magnitudes, with some overestimation in light EP and underestimation in heavy EP. Overall, across the historical period, EP has increased. The intensity and frequency of EP are projected to generally increase under different scenarios. At 1.5 °C warming levels, annual total precipitation in wet days (PRCP) increases by 5.74% (7.74%) under the SSP245 (SSP585). Additionally, as an EP becomes rarer, its rate of change rises. The main driving factors in EP are detected to be 30 hPa zonal wind (30ZW), relative number of sunspots (SF), south Asian summer monsoon (SAM), sea surface temperature anomaly in the region of 5°S-5°N, 170°-120°W (NINO 3.4), and mean surface temperature (T). [Display omitted] • The local intensity scaling method is an excellent choice for correcting precipitation bias in model products. • The intensity and frequency of extreme precipitation (EP) are projected to increase further. • The rate of change in EP increases with the rareness of events. • The main driving factors associated with EP are 30ZW, SF, SAM, NINO3.4, and T. [ABSTRACT FROM AUTHOR]

Major floods in Spain in September 9–13, 2019 resulted in seven casualties and massive losses to agriculture, property and infrastructure. This paper investigates the utility of satellite data to: (1) characterize the event when input into a hydrological model, and to provide an accurate picture of the evolution of the floods; and (2) inform meteorologists in real time in order to complement model forecasts. It is shown that the precipitation estimates from the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO, available since 2014) and the merged satellite estimates provide an extraordinary improvement over previous technologies to monitor severe hydrometeorological episodes in near real time. In spite of known biases and errors, these new satellite precipitation estimates can be of broad practical interest to deal with emergencies and long-term readiness, especially for semi-arid areas potentially affected by ongoing global warming. Comparisons of satellite data of the September event with model outputs and more direct observations such as rain gauges and ground radars reinforce the idea that satellites are fundamental for an appropriate management of hydrometeorological events. • The IMERG product is useful for monitoring severe storms in the Mediterranean. • Integration of satellite precipitation data into models can save lives and assists planning. • The integration of satellite estimates with numerical forecasts seems unavoidable. • More research on the effects of deep convection over land is required. [ABSTRACT FROM AUTHOR]