the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 13 Aug 2025
Spatiotemporal variation in rainfall predictability in Serbia under a changing climate
Abstract. This study examines whether the predictability of precipitation dynamics in Serbia has been influenced by climate change. We apply Generalized Weighted Permutation Entropy (GWPE) to evaluate the temporal structure of daily precipitation series using the parameter q, which filters subsets of small (q < 0) and large (q > 0) fluctuations. The analysis covers data from 14 weather stations between 1961 and 2020.
Entropy values for q = 0 and q = 2, corresponding to Permutation Entropy and Weighted Permutation Entropy respectively, remained stable spatially and temporally. In contrast, GWPE values for q = -10 and q = 10, representing the predictability of small and large fluctuations, exhibited significant spatial and temporal variation between two 30-year subperiods. Entropy values for q = -10 were consistently lower, indicating that small precipitation fluctuations are more predictable than large ones. In several locations, significant changes in entropy occurred despite relatively stable annual precipitation amounts. In others, annual totals varied while entropy remained constant. These findings suggest that climate change has influenced the predictability of precipitation in Serbia. By filtering fluctuations across scales, GWPE effectively reveals underlying changes that may be masked by standard statistical measures.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3766', Suzana M Blesic, 28 Sep 2025
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RC2: 'Comment on egusphere-2025-3766', Anonymous Referee #2, 10 Oct 2025
This manuscript offers an interesting and original analysis of precipitation predictability in Serbia under climate change. By combining PE, WPE, GWPE, and statistical complexity across different fluctuation scales, the study provides a novel perspective and yields insightful results that deepen our understanding of precipitation’s statistical predictability. I recommend publication after the authors address several important issues noted below.
- The authors have carried out a detailed and multi-dimensional analysis. However, the discussion mainly focuses on describing the results, with less attention to the underlying causes. It would strengthen the paper if the authors could further elaborate on the possible drivers behind the observed changes in predictability between the two periods and discuss the reasons for the spatial distribution patterns. Since only 14 sites are included, it might also be useful to include the discussion of the resulting uncertainty.
- The introduction offers rich background information on CECP and then introduces the GWPE method. To improve readability, it may help to clarify the conceptual or methodological link between these two frameworks, as the transition between lines 30–60 currently feels somewhat abrupt. It would be helpful for the authors to reorganize the two sections and clarify their connection and application to improve coherence.
- The choice of 1990 as the dividing year between the two study periods could be explained more clearly. Would the results differ if a different division year were chosen? How sensitive are the findings to this temporal segmentation? Would analyzing the entire period as a whole yield consistent outcome?
- For Figure 2 (and line 145), arranging the precipitation histogram from the lowest to highest mean annual precipitation might make the result easier to interpret. Currently, the order seems somewhat random. In addition, Figure 2 shows that some stations (e.g., KR and NE) had higher precipitation in 1991–2020 than in 1961–1990, whereas the legend in Figure 3 suggests that all differences are positive—please verify whether the legend is correct. It would also be helpful if the captions for Figures 2 and 3 included more information (e.g., data sources and calculation methods). Adding station names to the map in Figure 3 could further assist in comparing with Figure 2.
- The font sizes and overall layout of Figures 3–8 could be adjusted to improve readability, as some text is currently quite small. The figure captions are also somewhat too brief and could include more explanatory detail—such as what each variable represents and how the figures should be interpreted—to assist readers who may be less familiar with the methodology.
- The meaning of the GWPEC coordinate axes in Figures 6 and 7 could be clarified. Although statistical complexity (GWPEC) is mentioned in lines 185–200, it is not defined in the Methods section. Including a concise explanation of how this metric is calculated and interpreted would make the analysis easier to follow. Similar clarifications would be helpful for abbreviations such as CWCECP and GWCECP (line 175), which appear without prior definition or explanation.
- Finally, since several figures present similar subplots, it might be worth simplifying and refining them to improve overall clarity and visual quality. For example, combining Figures 7 and 8 might allow for a more intuitive comparison between the two time periods, and merging Figures 2 and 3 might help present the information more comprehensively.
Citation: https://doi.org/10.5194/egusphere-2025-3766-RC2
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I find this manuscript and its findings both interesting and valuable for advancing our understanding of the complexity and predictability of rainfall records, particularly in light of the current uncertainty surrounding general rainfall predictions. The methods used for data analysis are appropriate and reproducible, and I especially commend the application of the relatively new entropy-based calculation method. Overall, I recommend this manuscript for publication in EGUsphere.
My questions and comments are as follows:
The use of two climatic time periods for separate analyses is clear. However, I am also interested in whether analyzing the entire available period (1961–2020) would reveal visible changes in the GWPE plots (e.g., crossovers) that might indicate shifts in predictability between the two climatic periods. Could you provide results or insights for the whole period?
The GWPE values are presented with three significant digits. Does this imply that the error of calculation lies in the third digit? More generally, is there a way to estimate or quantify the error associated with this method?
In Figures 3, 8, and 9, would it be possible to display the differences on a color scale distinct from those used for rainfall amount or GWPEC in the two periods? Similarly, in Tables 2 and 3, could you add columns showing the differences in values between the two periods?
For q>0, GWPE values all appear higher than 0.5, except for Sremska Mitrovica during the 1991–2020 period. This suggests a possible pattern—has a similar behavior been observed in other types of real-world data?
Thank you for your responses and for the valuable contribution to this special issue.