Determining seasonal ranges and climate change in North Macedonia (1951–2024)

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To the best of our knowledge, this is the first study in North Macedonia to analyze seasonal dynamics using such a statistical-meteorological approach. The study used data on daily temperature series and seasonal durations, obtained for the period 1951–2024, from the main meteorological station in Prilep (National Hydrometeorological Service, NHMS‑Skopje, North Macedonia). The processing applied quartile measures of position, Student’s t‑test, and Fisher’s F‑test statistics. The range of seasons is defined by applying a quartile distribution of temperature. Furthermore, based on mathematical‑statistical analysis of the seasons, statistically significant differences were investigated between the previous (1951–1969, 1969–1987, 1979–1997, 1987–2005) and recent (2006–2024) period. Significant differences in the mean values and variances of the summer range were observed. The increase in the mean values in the more recent period indicates an expansion of the seasonal range, while the decrease in variance reflects greater stability. In winter, increased emissions from heating and traffic created local thermal effects and statistical analysis showed a decrease in the winter range. Significant statistical differences indicate a structural change in seasonal dynamics. The cumulative effect of global warming, urbanization, and changes in the atmospheric circulation resulted in a more stable but warmer summer regime and shorter, warmer winters. These findings align with global evidence of a reduction in temperature variability coupled with an increase in the occurrence and severity of extreme heat events in recent decades (Zhou et al. 2024 ). Overall, the results demonstrate a shift toward a more thermally stable yet warmer climate regime, consistent with the impacts of contemporary anthropogenic climate change. Seasons range quartiles Student T test Fisher F test anthropogenic climate change Figures Figure 1 Introduction Climate change that is attributed directly or indirectly to human activity and affects the composition of the global atmosphere and the natural climate variability observed over comparable time periods is called anthropogenic climate change (IPCC 2023; UNFCCC 1992; Tang et al. 2025 ). This phenomenon is usually identified by long‑term trends that deviate significantly from historical baseline values (Diffenbaugh et al. 2011; Screen et al. 2013; Wibig et al. 2025; You et al. 2021; Gu et al. 2023 ). Natural atmospheric oscillations, including the El Niño–Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), exhibit quasi‑periodic behavior that modulate regional and global climate patterns. Spatiotemporal modeling of these oscillations reveals cyclical fluctuations in key physical parameters (Polyakova et al. 2006 ). However, the influence of anthropogenic activities — primarily through greenhouse gas emissions and land‑use changes — has disrupted the regularity of these cycles (Santoso et al. 2017 ; Liu et al. 2023 ; Lu et al. 2025 ). Observed anomalies include enhanced oscillation magnitudes, altered periodicity, and increased irregularity, suggesting a systemic disruption of Earth’s climate‑regulating mechanisms. For example, model projections suggest that the NAO becomes more positive and variable with increasing greenhouse gas concentrations (McKenna et al. 2022 ; Zhao et al. 2025 ). Similarly, ENSO studies reveal intensified variability and more frequent extreme El Niño events under the influence of global warming (Cai et al. 2014 ; Yang et al. 2021 ). Seasonal dynamics have also undergone significant transformations. The duration and intensity of seasons now reflect altered regimes of historical stability, with longer summers, shorter winters, and the emergence of anomalous weather phenomena (Wang et al. 2021 ; Guan et al. 2021 ; Choi et al. 2023 ). In some areas, such as the Arabian Peninsula, summers are becoming warmer while winters become shorter and milder (Alghamdi 2024 ; Almazroui et al. 2024 ). Similar changes are evident in lake ecosystems, where earlier spring warming and delayed autumn cooling prolong the summer period (Woolway et al. 2023 ). The increased frequency of anomalous or extreme seasonal events, especially in the Arctic, further indicates systemic deviations from the historical climate state (Overland 2024 ). These changes imply a deviation from the thermodynamic equilibrium that the Earth’s climate system has historically maintained. According to the principles of thermodynamics, natural systems tend towards equilibrium (Zhuo et al. 2025 ). However, anthropogenic perturbations cause artificial imbalances, forcing the climate system to recalibrate. This manifests in altered seasonal cycles and extreme weather events, which can be interpreted as an adaptive response of nature aimed at restoring the system’s historical equilibrium. For example, increasing frequencies of atmospheric circulation patterns in the North Atlantic are correlated with more intense heat waves and storms in Europe, consistent with shifts from previous climate equilibria (Donat et al. 2022 ). In addition, the seasonal nature of extreme precipitation is predicted to change, with extremes occurring at different times of the year, implying altered cyclical stability in climate regimes. Various attempts have been made to address this issue when defining the range of seasons based on global temperature in Europe and the Balkans. The Köppen classification (1936) represents one of the first systematic attempts to define climate types based on temperature and precipitation thresholds. This approach allows climate differentiation to be based on objective and measurable criteria. In more recent research, the concept of using temperature thresholds has been extended to analyses of seasonality. One of the most relevant studies on defining the beginning and end of annual seasons in Europe according to temperature thresholds is the research of most authors in the study by Miksovsky et al. 2020 . Körner (2023) and Leeper (2021) used functional temperature thresholds (e.g., 0, 5, 10, 15°C) to define growing seasons and other biophysical parameters (Körner et al. 2023 ; Leeper et al. 2021 ; Kejna et al. 2023). Percentage thresholds were used and were important when analyzing extremes or comparing regions; for example, the 90th percentile of daily maximum temperatures for “warm days” or “warm seasons” (Mahlstein et al. 2015 ; Brunner et al. 2024). Dynamic methods were used, i.e., thresholds that adapt to chronological-spatial variations (changing over the years or defined according to calendar days or local context) (Huang et al. 2019 ; Fan 2025 ; Brázdil et al. 2009 ). Objective/synoptic methods were also applied, using reanalyses and classification of synoptic types, incorporating not only temperature but also atmospheric variables, humidity, cloudiness, etc., to define “seasons” in a climatological sense (Kwon et al. 2023 ; Kotsias et al. 2023 ; Bissolli et al. 2001). To determine the beginning and end of the seasons, temperature and homogenization thresholds were employed (Li et al. 2009). A global analysis of extreme indices and seasonal changes based on daily temperature thresholds was also conducted (Koch et al. 2003; Nazeri 2025). Materials and methods Study area The study was conducted using observations from the Main Meteorological Station (MMS) in Prilep, operated by the National Hydrometeorological Service (NHMS) of North Macedonia. This station was selected due to its long-term continuous operation, adherence to World Meteorological Organization (WMO) standards, minimal urban influence on the local environment, and its representativeness for the central Pelagonia region. The geographical coordinates of the station are: latitude 41°20′ N, longitude 21°34′ E, and an elevation of 673 m. Its location in the Pelagonia Basin, flanked by mountainous terrain to the northeast and the Pelagonia Basin to the southwest, makes it well-suited for monitoring regional climate variability and trends (Milevski et al. 2018; Milevski et al. 2025 ). The area exhibits a continental–sub-Mediterranean climate, characterized by warm, dry summers and cold winters, making it particularly sensitive to climate fluctuations and extreme meteorological events (Lazarevski 1993 ). Combined with its long-term, standardized data set, the Prilep MMS provides a robust basis for statistical analysis of climate change over the past seven decades. Methodology The methodology of this study comprises several key stages, including the acquisition and processing of temperature data, computation of multi-year averages or climatological normals, and determining of annual seasons and temperature-based thresholds. Seasonal determination is performed using temperature dynamics, quartile distributions, and analytical thresholds for spring, summer, autumn, and winter, while transitional sequences are also considered. Statistical analyses, including Student’s t-test for comparison of means, Fisher’s F-test for evaluation of variance homogeneity, and assessment of season duration, provide a robust framework for evaluating seasonal and climate characteristics. Temperature data, acquisition and processing This study used data on the mean daily air temperature collected from 1 January 1951 to 31 December 2024. The data were obtained from the Main Meteorological Station in Prilep, North Macedonia, provided by the NHMS‑Skopje. The temperature measurements were processed to characterize thermal conditions, assess long-term climate trends, determine seasonal thresholds, and evaluate evidence of climate change. Several methodologies are employed to calculate mean daily air temperatures, selection based on their accuracy and applicability to regional observation practices. The following formula, approved by the WMO (WMO 2018) is used in the NHMS in North Macedonia: Three-time daily sampling method: Tmean = (t07 + t14 + 2*t21) / 4 (1) This formula (1), which assigns double weight to the evening observation (21:00), is the standard method for calculating the climatological daily mean air temperature. Multi-year averages or climatological normals Monthly and daily averaged temperatures, serve as benchmarks for detecting anomalies and assessing long-term climate trends. These multi-year averages are essential for characterizing regional climate and for evaluating deviations that may indicate anthropogenic climate change (Barry 2009 ; Lutgens 2019 ; WMO 2017). Astronomical seasons and thresholds In temperate latitudes, the annual cycle is traditionally divided into four seasons—spring, summer, autumn, and winter—based on astronomical criteria. These divisions result from the axial tilt of the Earth relative to the ecliptic plane, which governs the distribution of solar radiation throughout the year (Meeus 1991 ). According to this framework, the seasons begin on fixed calendar dates: winter, 21 December; spring, 23 March; summer, 21 June; and autumn, 23 September. Each season is assumed to cover approximately one quarter of the year, with a nominally equal duration (Table 1 ). However, this astronomical model does not account for interannual variability of surface temperatures, nor does it reflect the influence of climatic or anthropogenic factors on seasonal dynamics. Seasonal durations, expressed in per mille (‰) of astronomical annual days, are presented in Table 1 . Table 1 The seasonal duration expressed in ‰. Season Duration (‰) Spring 247 Summer 258 Autumn 244 Spring 247 The astronomical demarcation yields subtle asymmetries in seasonal extents, with summer extending up to 258‰ of the annual cycle—attributed to the eccentricity of the Earth's orbit and the alignment of perihelion with the boreal summer—while autumn is contracted to the shortest span of 244‰. Such inequalities, although small relative to nominal quarterly divisions, highlight the geometric foundations of the calendar and their deviation from thermal or phenological realities. Summer is the longest in duration, followed by winter, then spring, and autumn. Statistical approaches for seasonal and climate analysis The discipline of statistics, though conceptually sophisticated, has become a cornerstone of empirical science. Originating in the mid-18th century through the works of Gottfried Achenwall, the field has undergone substantial methodological evolution. In atmospheric sciences, the integration of statistical reasoning into meteorology has given rise to meteorological statistics—a specialized subfield concerned with the quantitative analysis of meteorological phenomena. These methods enable the interpretation of extensive observational datasets, facilitating the identification of patterns, anomalies, and long-term trends, which are critical for hypothesis testing, evaluating climate variability, and detecting anthropogenic signals in climate systems (Wilks 2011 ; von Storch et al. 1999; Gong 2002 ; Della Marta 2008; Moore 2017; Mann 2016 ; Triola 2018 ; Markovic 1973 ; Ivanovic 1976). In this study, the analysis began with the application of quartile statistics to determine seasonal thresholds, followed by the use of Student’s t-test and Fisher’s F-test to assess homogeneity and compare distinct temporal periods, thereby providing evidence for the anthropogenic character of recent climate changes. Seasonal determination based on temperature dynamics The determination of seasons based on temperature dynamics requires a statistically robust methodology, which assumes that the reference period represents predominantly natural climate variability. In this study, the seasonal cycle is defined using quartile-based thresholds derived from a climatologically stable reference period (1951–1980), assumed to reflect primarily natural atmospheric oscillations with minimal anthropogenic influence (Piotrowicz 2001 ). Reference period and statistical framework The reference period 1951–1980 was selected due to its relative climatic stability and lower anthropogenic impact. Statistical parameters derived from this period serve as benchmarks for identifying seasonal transitions throughout the study period (1951–2024). The primary statistical tool employed is the quartile position measure, which characterizes the distribution of mean daily temperatures for each calendar month. For the Prilep MMS, the quartile distribution of mean daily temperatures was calculated for each month of the reference period (Table 2 ). These quartile values form the basis for defining temperature thresholds that delineate the beginning and end of each season. Table 2 Quartile distribution of mean daily temperatures by month, Prilep municipality (1951–1980). Month Min (25%) Median (50%) (75%) Max Jan −18.2 −2.8 0.2 3.2 11.8 Feb −14.6 −0.7 2.4 5.7 14.8 Mar −8.9 3.0 5.8 8.7 24.9 Apr −0.8 8.0 10.6 13.1 19.9 May 5.0 12.9 15.5 18.0 25.3 Jun 8.5 17.3 19.7 21.9 28.6 Jul 10.2 19.7 21.8 23.8 30.3 Aug 10.8 19.2 21.7 24.0 29.2 Sep 5.0 15.1 17.6 19.6 27.6 Oct −0.3 9.3 11.7 14.2 21.2 Nov −6.3 4.3 7.4 10.1 16.6 Dec −17.7 −1.0 2.2 5.3 15.1 Statistical assessment of season duration The homogeneity of season duration data was assessed by comparing two time periods: a previous period (1951–1987) and a recent period (1988–2024) using simple statistical methods. Student’s t-test and Fisher’s F-test were applied to investigate the relationship between sub-periods (1951–1969, 1969–1987, 1979–1997, 1987–2005) and the recent period (2006–2024), with the aim of determining whether statistically significant changes in seasonal duration distributions occurred. These analyses allowed for an empirical assessment of potential changes in climatic conditions attributable to anthropogenic influences. Student’s t-test: comparison of means To assess changes in seasonal duration, a two-tailed Student’s t-test for means with unequal variances was employed (Student 1908 ; Mann 2016 ; Triola 2018 ; Markovic 1973 ). This method is particularly suitable for detecting changes in continuous variables between two related time frames and is widely applied in environmental studies to evaluate the influence of external factors such as anthropogenic climate change. The results of the t-test are summarized in the Results section. Fisher’s F-test: evaluation of homogeneity of variance Complementing the analysis of means, the Fisher F-test for equality of variances was used to determine whether the dispersion of seasonal durations differed significantly between the periods considered (Fisher 1925 ; Mann 2016 ; Triola 2018 ; Markovic 1973 ; Box 1953 ; Montgomery et al. 2014; Wilks 2019 ). This test is crucial for assessing the stability of climate variability and identifying potential increases in the frequency or intensity of extreme events. Results of the F-test are presented in the Results section. Analytical determination of seasons Classification of seasons is a fundamental component of regional climatological analysis (Trenberth 1983 ), particularly in heterogeneous terrains such as North Macedonia, where Mediterranean, continental, and mountainous climates coexist across a wide altitudinal gradient (60–1800 m a.s.l.) according to available meteorological stations. Given this climatic diversity, applying uniform seasonal thresholds based solely on mid-quartile (50%) temperature values is insufficient to capture local nuances (Wilks 2011 ). Contemporary research increasingly utilizes quantile-based methods or analyses of changes in seasonal onsets and terminations to improve precision (Zhang et al. 2019 ). This study adopts a geographically sensitive approach to defining annual seasons, focusing on the Prilep MMS (673 m a.s.l.), influenced by a continental–sub-Mediterranean climate. Seasonal thresholds were obtained using a combination of long-term temperature averages from the reference period 1961–1990 (Table 3 ) and quartile distribution parameters calculated individually for each station (Table 2 ). Spring and winter thresholds Considering transitional months according to the astronomical definition, the beginning of spring is in March. For stations at 60–700 m a.s.l., the spring threshold was set at approximately 9°C (near the upper limit of observed March temperatures in Table 3 ), corresponding to the 75th percentile of March temperatures (8.7°C) in Table 2 , reflecting the warmer end of early spring. For stations at 700–1800 m a.s.l., thresholds were set near the upper limit of observed March temperatures (5–6°C; Table 3 ). Similarly, the winter threshold was defined using the 75th percentile of December temperatures, set at 5.3°C, while the 50th percentile was 2.2°C (Table 2 ). This adjustment accounts for variability across stations, aligning the threshold with the upper range of observed December temperatures (0.6–4.9°C; Table 3 ) and ensuring more accurate detection of sustained cold conditions. For higher elevations (700–1800 m a.s.l.), thresholds were close to the upper limit of observed December temperatures (-1.7–3.3°C; Table 3 ). This methodology ensures that seasonal boundaries are adapted to the thermal realities of transitional months rather than being arbitrarily fixed, enhancing the climatological fidelity of seasonal classification. Summer and Autumn Thresholds For summer and autumn, the 50th percentile (median) of temperature values was considered adequate for determining seasonal thresholds. Based on Table 3 , the threshold for the onset of summer (June) was set at 19.6°C, which closely approximates the mean of observed June temperatures across stations (range: 17.2–22.8°C; Table 3 ). Table 3 Average air temperatures North Macedonia (1961/90). H(m) Meteo. station III VI IX XII 59 Gevgelija 8.7 21.9 20.1 4.8 100 Valandovo 9.2 22.8 21 4.9 125 Demir Kapija 8.6 22.2 20.1 3.2 175 Veles 8.1 21.9 19.9 3 180 Nov Dojran 8.3 22.1 20.6 5.2 224 Strumica 7.9 21.5 19 2.2 232 Skopje P. 7.5 20.7 18.6 1.3 250 Amzabegovo 7.5 21.1 19.2 2.5 260 Kavadarci 8.1 22.1 19.8 3 260 Katlanovo 7.1 20.5 18 1.7 301 Skopje R. Hill 7.5 20.8 18.7 2.2 326 Štip 7.5 21.1 19.2 2.4 338 Kumanovo 6.7 20.2 18 1.5 345 Kočani 8.3 21.4 19.4 2.9 380 Radoviš 7 20.6 18.6 2.6 462 Tetovo 6.5 19.2 16.9 0.7 525 Gostivar 5.9 18.8 16.1 0.6 545 Makedonski Brod 5.8 17.9 16 1.4 586 Bitola 6.3 19.5 17.2 1 620 Kičevo 6.1 18.5 16.6 1.5 630 Delčevo 5.3 18.3 16 1.2 640 Kratovo 6.1 18.7 17.6 2 673 Prilep 6 19.2 17.5 1.6 675 Debar 6.5 19.1 17.8 2.1 691 Kriva Palanka 5 17.6 15.9 1.2 695 Struga 5.6 18.2 16.4 2.7 760 Ohrid 5.8 18.3 16.9 3.3 824 Berovo 3.6 16.3 13.9 0.3 881 Resen 4.2 16.8 14.9 1.9 1230 Kruševo 2.6 15.6 14.6 0.4 1280 Mavrovo 1.4 14.2 12.7 -0.7 1340 Lazaropole 1.4 13.6 12.2 -0.5 1750 Popova Šapka -1.1 11.1 10.2 -1.7 For the onset of autumn (September), the threshold was set at 17.6°C, closely corresponding to the mean of observed September temperatures across stations (16.0–21.0°C; Table 3 ). For stations at 700–1800 m, thresholds were determined using the 50th percentile values from Table 2 . This methodology ensures that seasonal boundaries are not arbitrarily fixed but are adapted to the thermal realities of transitional months, thereby improving the climatological fidelity of seasonal classification. These thresholds represent the average climatic conditions typical of the respective months and align with established climatological practices for defining seasonal transitions. Seasonal thresholds and detection criteria To identify the beginning and end of each season in a given year, the following criteria were applied: A sequence of six consecutive days with mean daily temperatures above the respective seasonal threshold indicates the onset of spring, summer, or autumn. A sequence of six consecutive days with temperatures below the winter threshold indicates the onset of winter. The end of each season is defined as the day preceding the start of the next season, based on the same six-day rule. This approach ensures that transient temperature fluctuations do not artificially alter seasonal boundaries and that only sustained thermal conditions define seasonal transitions. Consideration of transitional sequences In cases where anomalous sequences lasting more than six days and falling outside the defined seasonal thresholds, occurred after the onset of a season, these sequences were flagged for further investigation. Although not analyzed in this study, such events may serve as indicators of seasonal variability and climate instability, warranting future research. Seasonal duration and normalization Once the start and end dates of each season were determined, the total number of days comprising each season was calculated. To account for unequal lengths of calendar months and to facilitate interannual comparisons, average seasonal durations are expressed in per mille (‰) of the total annual days, providing a normalized series for statistical analysis. Results Temperature thresholds and seasonal duration Temperature thresholds for defining the onset of the four annual seasons were determined using the quartile distributions of mean daily air temperatures, calculated for the reference period 1951–1980. The resulting thresholds for spring, summer, autumn, and winter are summarized in Table 4 . These values represent statistically derived transition points based on the thermal characteristics of the climatologically stable reference interval and form the basis for subsequent seasonal delineation. Using the seasonal detection methodology described in the preceding section, the duration of each season was computed for the full study period (1951–2024). Seasonal lengths were derived from the quartile-based thresholds and expressed in per mille (‰) of annual days, enabling direct comparison across years with differing seasonal patterns. Table 4 Temperature thresholds according quartile distribution, MMS Prilep 1951–1980. Season Temperature thresholds Spring 8.7°C (March) Summer 19.7°C (June) Autumn 17.6°C (September) Winter 5.3°C (December) The results annualy are illustrated in Graphic 1 and Graphic 2. Spring duration exhibited considerable interannual variability, ranging from 100 to 295‰, with both the maximum and minimum values occurring in the earlier historical periods. Summer duration ranged from 112 to 316‰, with the longest summers recorded in the recent period and the shortest summers in the earlier periods. Autumn duration varied between 70 and 252‰, displaying a similar pattern: the maximum in the recent period and the minimum in the historical periods. Winter duration ranged from 168 to 402‰, with both extremes occurring in the more recent period, indicating an increased variability in winter length relative to the mid-20th-century baseline. Graph 1. Seasonal range in days by year, Prilep 1951–2024 spring-summer, NHMS, Skopje. Graph 2. Seasonal range in days by year, Prilep 1951–2024 autumn-winter, NHMS, Skopje. The range of seasonal durations for the period 1951–2024 was expressed in per mille (‰) of annual days to normalize interannual variability and to avoid distortions arising from unequal month lengths. This normalization enables a consistent comparison of seasonal extents across the full study interval. The mean seasonal durations for the two climatologically distinct periods—1951–1987 and 1988–2024 are presented in Table 5 . Table 5 Average duration of seasons expressed in per mille (‰). Seasons 1951/1987 (‰) 1988/2024 (‰) Spring 212 207 Summer 258 283 Autumn 186 177 Winter 344 333 The deviation (contrast) between individual seasons was also quantified and expressed in per mille (‰), with the results summarized in Table 6 . Table 6 Contrast between the seasons Prilep, 1951–2024. Period Deviation (‰) Deviation (‰) Spring/Autumn Summer/Winter 1951/1987 26 86 1988/2024 30 50 In addition, a comparative analysis of five-year mean seasonal contrasts was conducted for the entire 1951–2025 period. These contrasts, also expressed in per mille (‰), are illustrated in Graph 3. Graph 3. Five-year deviations between Spring and Autumn and between Summer and Winter for the period 1951–2025 at the Prilep MMS. Student’s t-test with unequal variances To evaluate whether the duration of thermal seasons has undergone statistically significant shifts over time, Student’s t-tests with unequal variances (two-tailed) were applied to annual seasonal duration series from the Prilep MMS. Four historical intervals (1951–1969, 1969–1987, 1979–1997, and 1987–2005) were each compared with the recent period (2006–2024). This test, suitable for samples with heterogeneous variances, assesses whether the mean difference in seasonal length between two periods differs significantly from zero at the 95% confidence level (α = 0.05). For each comparison, the degrees of freedom (df), t-statistic, p-value, critical t-value, and the 95% confidence interval for the mean difference (M₂ – M₁) were computed (Student 1908 ; Fisher 1925 ; Box 1953 ; Montgomery et al. 2014; Wilks 2019 ; Marković 1973). The resulting statistics provide an empirical basis for detecting structural changes in seasonal duration and for interpreting their climatological significance. The results of the Student’s Welch t-test for spring at the Prilep MMS (NHMS, Skopje) indicate that there are no statistically significant differences in mean seasonal durations between the historical periods (1951–1969, 1969–1987, 1979–1997, and 1987–2005) and the recent period (2006–2024) (Table 7 ). For example, the comparison between 1951–1969 and 2006–2024 yielded a t‑statistic (tstat) of 0.54, with a critical t‑value (tcrit) of 2.03. Since tstat < tcrit, the null hypothesis of equal mean seasonal durations is not rejected. The corresponding p-value of 0.59 indicates a 59% probability of accepting the null hypothesis, confirming that there is no statistically significant change in the length of spring between the earlier and recent period. Table 7 Student’s t-test for Spring (comparison of periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 with 2006–2024). t-Test: Two-sample assuming unequal variances (Spring) 1951–1969 Variable 1 2006–2024 Variable 2 1969–1987 Variable 1 2006–2024 Variable 2 1979–1997 Variable 1 2006–2024 Variable 2 1987–2005 Variable 1 2006–2024 Variable 2 Mean 78 75 80 78 78 75 78 73 Variance 336 320 227 336 336 258 336 296 Observations 19 19 19 19 19 19 19 19 df 36 35 35 36 t Stat 0.54 0.31 0.48 0.83 P(T < = t) one-tail 0.30 0.38 0.32 0.21 t Critical one-tail 1.69 1.69 1.69 1.69 P(T < = t) two-tail 0.59 0.76 0.36 0.41 t Critical two-tail 2.03 2.03 2.03 2.03 CI = [-8.76, 15.07] CI = [-9.37, 12.74] CI = [-8.24, 13.61] CI = [-6.92, 16.50] The results of the Student’s t-test for the summer period at the Prilep meteorological station (NHMS, Skopje) indicate that the comparisons of 1951–1969 and 1987–2005 versus 2006–2024 show borderline statistically significant differences in mean seasonal durations. In contrast, the comparisons for 1969–1987 and 1979–1997 versus 2006–2024 reveal statistically significant differences (Table 8 ). For the autumn period, the Student’s t-test results indicate no statistically significant differences in mean seasonal durations between the historical periods (1951–1969, 1969–1987, 1979–1997, and 1987–2005) and the recent period (2006–2024) (Table 9 ). Table 8 Student’s t-test for Summer (comparison of periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 with 2006–2024). t-Test: Two-sample assuming unequal variances (Summer) 2006–2024 Variable 1 1951–1969 Variable 2 2006–2024 Variable 1 1969–1987 Variable 2 2006–2024 Variable 1 1979–1997 Variable 2 2006–2024 Variable 1 1987–2005 Variable 2 Mean 109 98 109 92 109 91 109 98 Variance 143 378 143 396 143 558 144 334 Observations 19 19 19 19 19 19 19 19 df 30 30 27 31 t Stat 2.04 3.05 2.81 2.16 P(T < = t) one-tail 0.03 0.00 0.00 0.02 t Critical one-tail 1.70 1.70 1.70 1.70 P(T < = t) two-tail 0.05 0.00 0.01 0.04 t Critical two-tail 2.04 2.04 2.05 2.04 CI = [-0.03, 21.39] CI = [5.37, 27.15] CI = [4.64, 29.58] CI = [0.62, 21.07] Table 9 Student’s t-test for Autumn, comparing the periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 with 2006–2024. t-Test: Two-sample assuming unequal variances (Autumn) 1951–1969 Variable 1 2006–2024 Variable 2 2006–2024 Variable 1 1969–1987 Variable 2 2006–2024 Variable 1 1979–1997 Variable 2 2006–2024 Variable 1 1987–2005 Variable 2 Mean 70 68 68 66 68 61 68 59 Variance 220 167 167 326 167 300 167 235 Observations 19 19 19 19 19 19 19 19 df 35 33 33 35 t Stat 0.38 0.37 1.39 1.94 P(T < = t) one-tail 0.35 0.36 0.09 0.03 t Critical one-tail 1.69 1.69 1.69 1.69 P(T < = t) two-tail 0.70 0.71 0.17 0.06 t Critical two-tail 2.03 2.03 2.03 2.03 CI = [-7.44, 10.91] CI = [-8.50, 12.28] CI = [-3.20, 16.99] CI = [-0.41, 18.30] The results of the Student’s t-test for the winter period at the Prilep meteorological station (NHMS, Skopje) indicate that the comparisons of 1951–1969, 1969–1987, and 1987–2005 versus 2006–2024 show statistically significant differences in mean seasonal durations. In contrast, the comparison of 1979–1997 versus 2006–2024 reveals no statistically significant difference (Table 10 ). Table 10 Student’s t-test for Winter, comparing the periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 with 2006–2024. t-Test: Two-sample assuming unequal variances (Winter) 1951–1969 Variable 1 2006–2024 Variable 2 1969–1987 Variable 1 2006–2024 Variable 2 1979–1997 Variable 1 2006–2024 Variable 2 1987–2005 Variable 1 2006–2024 Variable 2 Mean 123 110 128 110 117 110 134 110 Variance 188 334 368 334 927 334 429 334 Observations 19 19 19 19 19 19 19 19 df 33 36 29 35 t Stat 2.34 2.89 0.80 3.68 P(T < = t) one-tail 0.01 0.00 0.21 0.00 t Critical one-tail 1.69 1.69 1.70 1.69 P(T < = t) two-tail 0.03 0.01 0.43 0.00 t Critical two-tail 2.03 2.03 2.05 2.03 CI = [1.58, 22.94] CI = [5.24, 29.9] CI = [-10.14, 23.19] CI = [10.50, 36.24] F-test two sample Fisher’s F-test was applied to compare the variances of seasonal durations between the recent period (2006–2024) and the preceding periods (1951–1969, 1969–1987, 1979–1997, and 1987–2005) at the Prilep MMS (NHMS, Skopje). For spring, the F-values (1.05–1.48) are below the critical F-value of 2.22 at the 95% confidence level, and the one-tailed p-values (0.21–0.46) indicate no statistically significant differences in variance. The 95% confidence intervals for the variance ratios include unity, confirming that interannual variability in spring durations has remained largely stable over the analyzed periods (Table 11 ). Table 11 Fisher’s F-test for Spring, comparing the periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 with 2006–2024. t-Test: Two-sample for variances (Spring) 2006–2024 Variable 1 1951–1969 Variable 2 2006–2024 Variable 1 1969–1987 Variable 2 2006–2024 Variable 1 1979–1997 Variable 2 2006–2024 Variable 1 1987–2005 Variable 2 Mean 77 74 77 79 77 75 77 73 Variance 336 319 336 227 336 257 336 296 Observations 19 19 19 19 19 19 19 19 df 18 18 18 18 18 18 18 18 F 1.05 1.48 1.3 1.14 P(F < = f) one-tail 0.46 0.21 0.29 0.4 F Critical one-tail 2.22 2.22 2.22 2.22 CI = [0.40, 2.72] CI = [0.57, 3.84] CI = [0.50, 3.38] CI = [0.44, 2.95] Fisher’s F-test for the summer period at the Prilep MMS (NHMS, Skopje) shows statistically significant differences in the variances of seasonal durations for 1951–1969 and 1969–1987 compared with 2006–2024. For 1979–1997 and 1987–2005 versus 2006–2024, the differences are borderline significant. The F-values (2.19–2.76) exceed the critical F-value of 2.22, and the one-tailed p-values (0.02–0.05) support these observations. The 95% confidence intervals for the variance ratios further reject the null hypothesis as true (Table 12 ). Table 12 Fisher-F test for Summer (1951–1969, 1969–1987, 1979–1997 and 1987–2005 vs. 2006–2024). t-Test: Two-sample for variances (Summer) 1951–1969 Variable 1 2006–2024 Variable 2 1969–1987 Variable 1 2006–2024 Variable 2 1979–1997 Variable 1 2006–2024 Variable 2 1987–2005 Variable 1 2006–2024 Variable 2 Mean 98 109 93 109 97 109 98 109 Variance 378 143 396 143 314 143 334 144 Observations 19 19 19 19 19 19 19 19 df 18 18 18 18 18 18 18 18 F 2.64 2.76 2.19 2.3 P(F < = f) one-tail 0.02 0.02 0.05 0.04 F Critical one-tail 2.22 2.22 2.22 2.2 CI = [1.01, 6.80] CI = [1.06, 7.20] CI = [0.80, 5.70] CI = [0.90, 6.04] Fisher’s F-test for the autumn period at the Prilep MMS (NHMS, Skopje) indicates no statistically significant differences in the variances of seasonal durations for the periods 1951–1969, 1969–1987, 1979–1997, and 1987–2005 compared with 2006–2024. The F-values (1.31–1.94) are below the critical F-value of 2.22, and the one-tailed p-values (0.08–0.29) confirm that the null hypothesis of equal variances is not rejected. The 95% confidence intervals for the variance ratios also include unity, supporting the absence of significant changes in interannual variability for autumn (Table 13 ). Table 13 Fisher-F test for Autumn (1951–1969, 1969–1987, 1979–1997 and 1987–2005 vs. 2006–2024). t-Test: Two-sample for variances (Autumn) 1951–1969 Variable 1 2006–2024 Variable 2 1969–1987 Variable 1 2006–2024 Variable 2 1979–1997 Variable 1 2006–2024 Variable 2 1987–2005 Variable 1 2006–2024 Variable 2 Mean 70 68 66 68 61 68 59 68 Variance 220 167 327 168 300 168 235 168 Observations 19 19 19 19 19 19 19 19 df 18 18 18 18 18 18 18 18 F 1.31 1.94 1.79 1.4 P(F < = f) one-tail 0.29 0.08 0.11 0.24 F Critical one-tail 2.22 2.22 2.22 2.22 CI = [0.50, 3.40] CI = [0.75, 5.05] CI = [0.68, 4.64] CI = [0.54,3.64] Analysis of variances using Fisher’s F-test for the winter period at the Prilep MMS (NHMS, Skopje) demonstrates that the distributions of seasonal durations for 1951–1969, 1969–1987, 1979–1997, and 1987–2005 are statistically comparable with those of 2006–2024. All F-values (1.1–1.77) remain below the critical threshold of 2.22, and the corresponding one-tailed p-values (0.12–0.42) indicate that the null hypothesis of equal variances is not rejected. Additionally, the 95% confidence intervals for the variance ratios encompass unity, confirming the stability of interannual variability in winter durations (Table 14 ). Table 14 Fisher-F test for Winter (1951–1969, 1969–1987, 1979–1997 and 1987–2005 vs. 2006–2024). t-Test: Two-sample for variances (Winter) 2006–2024 Variable 1 1951–1969 Variable 2 1969–1987 Variable 1 2006–2024 Variable 2 2006–2024 Variable 1 1979–1997 Variable 2 1987–2005 Variable 1 2006–2024 Variable 2 Mean 110 123 128 110 110 133 134 110 Variance 334 188 368 334 334 303 429 334 Observations 19 19 19 19 19 19 19 19 df 18 18 18 18 18 18 18 18 F 1.77 1.1 1.1 1.28 P(F < = f) one-tail 0.12 0.42 0.42 0.3 F Critical one-tail 2.22 2.22 2.22 2.22 CI = [0.68, 4.60] CI = [0.42, 2.85] CI = [0.35, 2.35] CI = [0.49, 3.33] Discussion The present analysis demonstrates pronounced changes in the duration of seasons at the Prilep MMS over the period 1951–2024. Seasonal onsets and terminations were determined using temperature thresholds based on quartiles derived from a relatively stable pre-anthropogenic period (1951–1980), providing an objective and station-specific definition of seasonal limits grounded in the statistical distribution of observed temperatures. This approach allows for temporal comparisons across different periods and altitudes and avoids the arbitrariness of fixed temperature thresholds. Its primary strengths lie in its adaptability and objectivity; however, it is sensitive to extreme values if not properly preprocessed and does not directly incorporate physiological or bioclimatic thresholds such as 0°C or 25°C. Analysis of seasonal duration indicates that summer has lengthened significantly over the examined periods, increasing from an average of 92–98 days in earlier intervals to 109 days in the most recent period, with statistically significant differences confirmed by t-tests (t = 2.04–3.05; p ≤ 0.05). In contrast, winter has contracted, decreasing from 117–134 days to 110 days, with t-statistics exceeding critical values in multiple comparisons, indicating robust significance (t = 2.34–3.68; p ≤ 0.03). Comparisons for spring and autumn reveal relative stability in duration, suggesting that the principal seasonal shifts are concentrated in the extreme summer and winter periods. Borderline significant results were observed in some summer comparisons between older intervals and the recent period, highlighting potential temporal variability within the transitional phases of warming. Variance analysis further supports these findings. The F-test demonstrates that summer variance is significantly different between the pre-2006 periods and the most recent interval (F = 2.19–2.76; p ≤ 0.05), indicating a trend toward increased stability in summer duration, while variance in other seasons does not exhibit statistically significant changes. Collectively, these results indicate an overall compression of winter duration and extension of summer, consistent with an increased homogeneity in the annual temperature cycle. Such patterns align with observations from broader European and Balkan studies, which report asymmetric seasonal changes associated with anthropogenic warming (Brázdil et al. 2009 ; Bissolli et al. 2001; Unkašević et al. 2013; Gavrilov et al. 2015 ; Milevski et al. 2015 ; Tošić et al. 2021 ). The observed extension of summer and contraction of winter is likely attributable to rising mean temperatures, which facilitate surpassing summer heat thresholds and hinder maintenance of winter cold thresholds. Spring remains relatively unchanged, likely due to the stabilizing influence of prevailing westerly storm systems, whereas autumn exhibits a modest contraction, indicative of prolonged summer warmth before the transition to cooler conditions. The differential response of inter-seasons, reflected in the increased fractional year difference between spring and autumn, further suggests that warming exerts seasonally heterogeneous effects. The overall narrowing of inter-seasonal differences, particularly between summer and winter, reflects a more uniform annual thermal cycle, which is consistent with climate warming scenarios reported across the region. Long-term climatic drivers, including El Niño events and variability in the North Atlantic Oscillation, may have modulated these trends, particularly affecting the magnitude of summer–winter and spring–autumn contrasts (King et al. 2020 ; Jiang et al. 2024 ; Domeisen et al. 2025; Outten et al. 2024; Liu et al. 2025 ; Song et al. 2025 ). While these teleconnections offer explanatory insights, anthropogenic forcing remains the most plausible driver of the observed seasonal shifts over the 70-year period, given the limited influence of orbital and axial variations on decadal timescales (Schneider et al. 1974). Limitations of the study include reliance on a single station, which may not capture altitudinal and spatial heterogeneity across North Macedonia, ranging from low valleys to high mountain environments. While altitude-adjusted threshold rules have been proposed, their validity requires verification using multiple stations. Additionally, the six-day criterion for defining season onset, although operationally robust, may exclude short-term but climatically meaningful anomalies, leaving gaps in the characterization of extreme events such as prolonged heatwaves or dry spells, which have significant implications for agriculture and water management. To address these limitations, future research should incorporate data from multiple meteorological stations, supported by high-resolution reanalysis datasets such as ERA5, to evaluate the representativeness of observed seasonal changes and to refine temperature thresholds in relation to local climate variability. Future research should expand the spatial coverage to multiple meteorological stations and integrate high-resolution reanalysis datasets, enabling assessment of regional representativeness and refinement of altitude-specific temperature thresholds (Ananthu et al. 2025 ). Paleo-climatic proxies such as dendrochronology or sediment records could elucidate longer-term variability and disentangle anthropogenic impacts from natural climate oscillations. Furthermore, numerical climate models may simulate seasonal evolution under different greenhouse gas emission scenarios, informing adaptation strategies in the context of projected warming (Aleksova et al. 2024 ; Milevski et al. 2024 ; Aleksova et al. 2025 ; Milevski et al. 2025 ; Sabljić et al. 2025 ). Automated statistical or machine learning approaches could facilitate dynamic adjustment of seasonal thresholds, enhancing monitoring accuracy and resilience planning. The study underscores the value of data-driven, temperature-based seasonal definitions over rigid calendar constructs, providing an empirical framework for early detection of climate-induced seasonal shifts and supporting climate adaptation measures. Concluding remarks This study is the first in North Macedonia to apply quartile-derived temperature thresholds for defining thermal seasons, providing empirical rigor and revealing a selective reconfiguration of the annual cycle at the Prilep MMS over seven decades. Normalized as per mille (‰) fractions, these metrics circumvent calendar-based approaches and offer a template for phenological review. The greatest advantage of the method lies in its adaptability: by anchoring the thresholds to a previously forced baseline (1951–1980), anthropogenic signals are isolated, offering practical insights into transitions that astronomical paradigms cannot capture. These findings hold major implications for regional climatology, as homogenized seasons can precipitate cascading vulnerabilities—prolonged droughts threatening viticulture, unpredictable autumns disrupting harvest cycles, and weakened winters limiting snowpack recharge for water resources. In the heterogeneous terrain of North Macedonia, this framework elucidates altitudinal gradients in thermal partitioning, informing targeted adaptations that protect both agricultural yields and hydrological balances. Moreover, defining season lengths based on quartile temperature thresholds constitutes an additional metric for climate regionalization. The most common date in years when a particular temperature threshold is reached can be considered the general start of a given season. This approach enhances consistency in climate zone delineation by reflecting actual temperature dynamics rather than fixed calendar boundaries, allowing for improved alignment between climate classification and ecological or agricultural requirements (Tomczyk 2019 ). The applied method is based on quartiles that accommodate regions with varying temperature regimes and account for local context, although the choice of baseline periods exerts substantial influence, and thresholds may fluctuate over time. The singularity of the analysis constrains spatial generalization, and the lack of explicit examination of ENSO or NAO effects leaves external modulators underexplored, potentially confounding the attribution of observed variance. Despite these limitations, the study provides a foundational framework for further research, enabling the development of unambiguous links to radiative forcings and supporting evidence-based adaptation strategies to enhance resilience in climate-sensitive sectors. Recommendation The findings for Prilep are consistent with regional trends in Southeast Europe, where summers are becoming longer and warmer, and winters are shorter and milder (Lelieveld et al. 2016 ; Spinoni et al. 2018 ). The observed reduction in variability during the recent period can be interpreted as a “temporary stabilization,” reflecting a transitional phase in the local atmospheric response to climate change. Should emissions and pollution continue to increase, the current quasi-stable balance may be disrupted, potentially inducing compensatory atmospheric cooling via thermoregulatory mechanisms. Such phenomena have also been observed in climate oscillation models (Stevenson et al. 2022 ), wherein the system exhibits delayed responses to excessive warming through self-regulating feedback processes over extended timescales. Accordingly, these observations underscore the necessity for proactive human engagement in maintaining thermal equilibrium through mitigation of pollution and enhancement of energy efficiency. The stabilization of future climate conditions will depend upon collective human commitment, technological integration, and regional ecological planning. Implementation of agroclimatic zoning, temperature-based seasonality metrics, sustainable land-use practices, and strategic vegetation management provides a concrete pathway for sustaining a balanced climate system and mitigating the risk of compensatory cooling over forthcoming decades. Modern technological tools, including artificial intelligence, offer potential for the management of extreme climatic events and for the optimization of agricultural activities, including planting, harvesting, transportation, and land-use planning, tailored to local climate and soil conditions (Hansen et al. 2023 ; Olesen et al. 2002). The deployment of agroclimatic indicators and thermal sums enables efficient regionalization of agricultural production and facilitates the selection of crops with maximal climatic suitability (FAO 2021). The broader implication of this research is that effective restoration and preservation of natural systems necessitate coordinated global action, leveraging both societal and technological capacities to enhance agricultural productivity, tourism, and ecosystem resilience. This approach embodies the principle of “solving nature with nature” (Sachs 2020 ), which can be further strengthened by integrated educational and hybrid knowledge strategies aimed at fostering sustainable environmental stewardship and ensuring climate-resilient regional development. Declarations Compliance with Ethical Standards Contribution : All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Lidija Stojova, Ivancho Kaevski and Bojana Aleksova. The first draft of the manuscript was written by Lidija Stojova, and the other authors (Bojana Aleksova and Ivancho Kaevski) commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conflict of Interest: The authors declare no conflicts of interest. Ethical Conduct This is an observational study. Funding: This research received no external funding. Author Contribution All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Lidija Stojova, Ivancho Kaevski and Bojana Aleksova. The first draft of the manuscript was written by Lidija Stojova, and the other authors (Bojana Aleksova and Ivancho Kaevski) commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. 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Theoret Appl Climatol 138:1599–1613 Tošić I, Putniković S, Tošić M, Lazić I (2021) Extreme Temperature Events in Serbia in Relation to Atmospheric Circulation. Atmosphere 12(12):1584. https://doi.org/10.3390/atmos12121584 Trenberth KE (1983) What are the seasons? Bull Am Meteorol Soc 64(11):1276–1282 Triola MF (2018) Elementary Statistics, 13th ed. Pearson United Nations Framework Convention on Climate Change (UNFCCC) (1992) Article 1: Definitions. UNFCCC Unkašević M, Tošić I (2013) Trends in temperature indices over Serbia: Relationships to large-scale circulation patterns. Int J Climatol 33(15):3152–3161. https://doi.org/10.1002/joc.3652 von Storch H, Zwiers FW (1999) Statistical Analysis in Climate Research. Cambridge University Press Wang Y, Sun Y, Chen X, Chen Y, Xu Y (2021) Changing lengths of the four seasons by global warming. Geophys Res Lett 48(7). https://doi.org/10.1029/2020GL091753 . e2020GL091753 Wibig J, Jędruszkiewicz J (2025) Has climate change affected the occurrence of compound heat wave and heavy rainfall events in Poland? Sustainability 17(10):4447. https://doi.org/10.3390/su17104447 Wilks DS (2011) Statistical Methods in the Atmospheric Sciences, 3rd edn. Academic Wilks DS (2019) Statistical Methods in the Atmospheric Sciences, 4th edn. Academic, Amsterdam, Netherlands Woolway RI, Kraemer BM, Lenters JD, Merchant CJ, O’Reilly CM, Sharma S et al (2023) The pace of shifting seasons in lakes. Nat Commun 14(1):2119. https://doi.org/10.1038/s41467-023-37606-4 World Meteorological Organization (WMO) (2017) WMO Guidelines on the Calculation of Climate Normals (WMO-No. 1203). WMO, Geneva World Meteorological Organization (WMO) (2018) Guide to Meteorological Instruments and Methods of Observation (WMO-No. 8). WMO, Geneva Yang Y, Zhang R, Wang B (2021) Greenhouse warming intensifies North Tropical Atlantic climate variability. Sci Adv 7(35):eabg9690. https://doi.org/10.1126/sciadv.abg9690 You J, Wang S (2021) Higher probability of occurrence of hotter and shorter heat waves followed by heavy rainfall. Geophys Res Lett 48(17). https://doi.org/10.1029/2021GL094831 . e2021GL094831 Zhang X, Chen Y, Li Y, Song S (2019) Changes in the onset and withdrawal of the four seasons over China. J Clim 32(12):3885–3904. https://doi.org/10.1175/JCLI-D-18-0734.1 Zhao A, Brierley CM, Arra A, Shi Z, Hu A (2025) NAO in the PMIP: sensitivity to greenhouse gas forcing. EGUsphere, preprint. https://doi.org/10.5194/egusphere-2025-3140 Zhou J, Teuling AJ, Seneviratne SI, Hirsch AL (2024) Soil moisture–temperature coupling increases population exposure to future heatwaves. Earth’s Future 12(7). https://doi.org/10.1029/2024EF004697 . e2024EF004697 Zhuo W, Sánchez-Benítez A, Athanase M, Jung T, Yao Y, Goessling HF (2025) Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes. npj Clim Atmospheric Sci 8 Article 169. https://doi.org/10.1038/s41612-025-01031-x Graphs Graph 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Graph13.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9039534","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639251448,"identity":"80e65ded-a8a1-46bb-bf81-9eed566c4482","order_by":0,"name":"Lidija Stojova","email":"","orcid":"","institution":"Department of Meteorology, National Hydrometeorological Service (NHMS)","correspondingAuthor":false,"prefix":"","firstName":"Lidija","middleName":"","lastName":"Stojova","suffix":""},{"id":639251449,"identity":"d440eb7e-db64-4ec5-b140-c38b2993bdef","order_by":1,"name":"Ivancho Kaevski","email":"","orcid":"","institution":"Hydro-Energo-Engineering, (HEI)","correspondingAuthor":false,"prefix":"","firstName":"Ivancho","middleName":"","lastName":"Kaevski","suffix":""},{"id":639251450,"identity":"bef255b1-f439-4c95-8f89-55399121332a","order_by":2,"name":"Bojana Aleksova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYDACdgZmCEOCgcEASMkxMPAQ0MKMpsWYNC0gkNhASAt/M/Nhwx8Vh+UZpJsPFPzcYZe+4fjZgw8+MNjJ6TZg1yJxmC05mefMYcMGmWMJhr1nknM3nMlLNpzBkGxsdgCHNYd5jA8ztqUxNkjkGBjwtjHnbjiQYybNw3AgcRsOLfKH+T8f/NmWZg/SYvi3rT7d4Pwb/FoMDvMwJ/C22SSCtBjzth1OMLhBwBbDw2zGxjxnbJJBfjGWbTtuOPPGG2PDGQa4/SJ3vPmx5I8KCdsG6eZjhm/bquX5zucYPvhQYSeH0/swYH+AgQ0UlQwKYJUGBJRDAfMDECnfQJzqUTAKRsEoGDkAAMIyWjzIbKEPAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Geography, Tourism and Hotel Management, Faculty of Sciences, University of Novi Sad","correspondingAuthor":true,"prefix":"","firstName":"Bojana","middleName":"","lastName":"Aleksova","suffix":""}],"badges":[],"createdAt":"2026-03-05 11:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9039534/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9039534/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109323905,"identity":"dad4e1ab-1c94-4a59-8b58-140817204706","added_by":"auto","created_at":"2026-05-15 14:10:50","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4182767,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area.\u003c/p\u003e","description":"","filename":"prilep1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9039534/v1/b03316cdd9f6fa9db5b5f12f.jpg"},{"id":109323983,"identity":"954a8c7f-a043-4dfd-bfbc-03ae7175eaec","added_by":"auto","created_at":"2026-05-15 14:11:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4898720,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9039534/v1/321d67f3-5b79-4f3f-af96-0373ea6a7b74.pdf"},{"id":109323906,"identity":"af8e7ddc-da1e-4824-8dec-066203d3679f","added_by":"auto","created_at":"2026-05-15 14:10:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24624,"visible":true,"origin":"","legend":"","description":"","filename":"Graph13.docx","url":"https://assets-eu.researchsquare.com/files/rs-9039534/v1/da20247157b4e53c92a70484.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determining seasonal ranges and climate change in North Macedonia (1951–2024)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate change that is attributed directly or indirectly to human activity and affects the composition of the global atmosphere and the natural climate variability observed over comparable time periods is called anthropogenic climate change (IPCC 2023; UNFCCC 1992; Tang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This phenomenon is usually identified by long‑term trends that deviate significantly from historical baseline values (Diffenbaugh et al. 2011; Screen et al. 2013; Wibig et al. 2025; You et al. 2021; Gu et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Natural atmospheric oscillations, including the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), exhibit quasi‑periodic behavior that modulate regional and global climate patterns. Spatiotemporal modeling of these oscillations reveals cyclical fluctuations in key physical parameters (Polyakova et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, the influence of anthropogenic activities \u0026mdash; primarily through greenhouse gas emissions and land‑use changes \u0026mdash; has disrupted the regularity of these cycles (Santoso et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObserved anomalies include enhanced oscillation magnitudes, altered periodicity, and increased irregularity, suggesting a systemic disruption of Earth\u0026rsquo;s climate‑regulating mechanisms. For example, model projections suggest that the NAO becomes more positive and variable with increasing greenhouse gas concentrations (McKenna et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, ENSO studies reveal intensified variability and more frequent extreme El Ni\u0026ntilde;o events under the influence of global warming (Cai et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Seasonal dynamics have also undergone significant transformations.\u003c/p\u003e \u003cp\u003eThe duration and intensity of seasons now reflect altered regimes of historical stability, with longer summers, shorter winters, and the emergence of anomalous weather phenomena (Wang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Guan et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Choi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In some areas, such as the Arabian Peninsula, summers are becoming warmer while winters become shorter and milder (Alghamdi \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Almazroui et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similar changes are evident in lake ecosystems, where earlier spring warming and delayed autumn cooling prolong the summer period (Woolway et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The increased frequency of anomalous or extreme seasonal events, especially in the Arctic, further indicates systemic deviations from the historical climate state (Overland \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These changes imply a deviation from the thermodynamic equilibrium that the Earth\u0026rsquo;s climate system has historically maintained. According to the principles of thermodynamics, natural systems tend towards equilibrium (Zhuo et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, anthropogenic perturbations cause artificial imbalances, forcing the climate system to recalibrate. This manifests in altered seasonal cycles and extreme weather events, which can be interpreted as an adaptive response of nature aimed at restoring the system\u0026rsquo;s historical equilibrium.\u003c/p\u003e \u003cp\u003eFor example, increasing frequencies of atmospheric circulation patterns in the North Atlantic are correlated with more intense heat waves and storms in Europe, consistent with shifts from previous climate equilibria (Donat et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the seasonal nature of extreme precipitation is predicted to change, with extremes occurring at different times of the year, implying altered cyclical stability in climate regimes. Various attempts have been made to address this issue when defining the range of seasons based on global temperature in Europe and the Balkans.\u003c/p\u003e \u003cp\u003eThe K\u0026ouml;ppen classification (1936) represents one of the first systematic attempts to define climate types based on temperature and precipitation thresholds. This approach allows climate differentiation to be based on objective and measurable criteria. In more recent research, the concept of using temperature thresholds has been extended to analyses of seasonality.\u003c/p\u003e \u003cp\u003eOne of the most relevant studies on defining the beginning and end of annual seasons in Europe according to temperature thresholds is the research of most authors in the study by Miksovsky et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e. K\u0026ouml;rner (2023) and Leeper (2021) used functional temperature thresholds (e.g., 0, 5, 10, 15\u0026deg;C) to define growing seasons and other biophysical parameters (K\u0026ouml;rner et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leeper et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kejna et al. 2023). Percentage thresholds were used and were important when analyzing extremes or comparing regions; for example, the 90th percentile of daily maximum temperatures for \u0026ldquo;warm days\u0026rdquo; or \u0026ldquo;warm seasons\u0026rdquo; (Mahlstein et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Brunner et al. 2024).\u003c/p\u003e \u003cp\u003eDynamic methods were used, i.e., thresholds that adapt to chronological-spatial variations (changing over the years or defined according to calendar days or local context) (Huang et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fan \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Br\u0026aacute;zdil et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Objective/synoptic methods were also applied, using reanalyses and classification of synoptic types, incorporating not only temperature but also atmospheric variables, humidity, cloudiness, etc., to define \u0026ldquo;seasons\u0026rdquo; in a climatological sense (Kwon et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kotsias et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bissolli et al. 2001). To determine the beginning and end of the seasons, temperature and homogenization thresholds were employed (Li et al. 2009). A global analysis of extreme indices and seasonal changes based on daily temperature thresholds was also conducted (Koch et al. 2003; Nazeri 2025).\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted using observations from the Main Meteorological Station (MMS) in Prilep, operated by the National Hydrometeorological Service (NHMS) of North Macedonia. This station was selected due to its long-term continuous operation, adherence to World Meteorological Organization (WMO) standards, minimal urban influence on the local environment, and its representativeness for the central Pelagonia region. The geographical coordinates of the station are: latitude 41\u0026deg;20\u0026prime; N, longitude 21\u0026deg;34\u0026prime; E, and an elevation of 673 m.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIts location in the Pelagonia Basin, flanked by mountainous terrain to the northeast and the Pelagonia Basin to the southwest, makes it well-suited for monitoring regional climate variability and trends (Milevski et al. 2018; Milevski et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The area exhibits a continental\u0026ndash;sub-Mediterranean climate, characterized by warm, dry summers and cold winters, making it particularly sensitive to climate fluctuations and extreme meteorological events (Lazarevski \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Combined with its long-term, standardized data set, the Prilep MMS provides a robust basis for statistical analysis of climate change over the past seven decades.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethodology\u003c/h3\u003e\n\u003cp\u003eThe methodology of this study comprises several key stages, including the acquisition and processing of temperature data, computation of multi-year averages or climatological normals, and determining of annual seasons and temperature-based thresholds. Seasonal determination is performed using temperature dynamics, quartile distributions, and analytical thresholds for spring, summer, autumn, and winter, while transitional sequences are also considered. Statistical analyses, including Student\u0026rsquo;s t-test for comparison of means, Fisher\u0026rsquo;s F-test for evaluation of variance homogeneity, and assessment of season duration, provide a robust framework for evaluating seasonal and climate characteristics.\u003c/p\u003e\n\u003ch3\u003eTemperature data, acquisition and processing\u003c/h3\u003e\n\u003cp\u003eThis study used data on the mean daily air temperature collected from 1 January 1951 to 31 December 2024. The data were obtained from the Main Meteorological Station in Prilep, North Macedonia, provided by the NHMS‑Skopje. The temperature measurements were processed to characterize thermal conditions, assess long-term climate trends, determine seasonal thresholds, and evaluate evidence of climate change.\u003c/p\u003e \u003cp\u003eSeveral methodologies are employed to calculate mean daily air temperatures, selection based on their accuracy and applicability to regional observation practices. The following formula, approved by the WMO (WMO 2018) is used in the NHMS in North Macedonia:\u003c/p\u003e \u003cp\u003eThree-time daily sampling method:\u003c/p\u003e \u003cp\u003eTmean = (t07\u0026thinsp;+\u0026thinsp;t14\u0026thinsp;+\u0026thinsp;2*t21) / 4 (1)\u003c/p\u003e \u003cp\u003eThis formula (1), which assigns double weight to the evening observation (21:00), is the standard method for calculating the climatological daily mean air temperature.\u003c/p\u003e\n\u003ch3\u003eMulti-year averages or climatological normals\u003c/h3\u003e\n\u003cp\u003eMonthly and daily averaged temperatures, serve as benchmarks for detecting anomalies and assessing long-term climate trends. These multi-year averages are essential for characterizing regional climate and for evaluating deviations that may indicate anthropogenic climate change (Barry \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lutgens \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; WMO 2017).\u003c/p\u003e\n\u003ch3\u003eAstronomical seasons and thresholds\u003c/h3\u003e\n\u003cp\u003eIn temperate latitudes, the annual cycle is traditionally divided into four seasons\u0026mdash;spring, summer, autumn, and winter\u0026mdash;based on astronomical criteria. These divisions result from the axial tilt of the Earth relative to the ecliptic plane, which governs the distribution of solar radiation throughout the year (Meeus \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). According to this framework, the seasons begin on fixed calendar dates: winter, 21 December; spring, 23 March; summer, 21 June; and autumn, 23 September. Each season is assumed to cover approximately one quarter of the year, with a nominally equal duration (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, this astronomical model does not account for interannual variability of surface temperatures, nor does it reflect the influence of climatic or anthropogenic factors on seasonal dynamics. Seasonal durations, expressed in per mille (\u0026permil;) of astronomical annual days, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe seasonal duration expressed in \u0026permil;.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuration (\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe astronomical demarcation yields subtle asymmetries in seasonal extents, with summer extending up to 258\u0026permil; of the annual cycle\u0026mdash;attributed to the eccentricity of the Earth's orbit and the alignment of perihelion with the boreal summer\u0026mdash;while autumn is contracted to the shortest span of 244\u0026permil;. Such inequalities, although small relative to nominal quarterly divisions, highlight the geometric foundations of the calendar and their deviation from thermal or phenological realities. Summer is the longest in duration, followed by winter, then spring, and autumn.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical approaches for seasonal and climate analysis\u003c/h2\u003e \u003cp\u003eThe discipline of statistics, though conceptually sophisticated, has become a cornerstone of empirical science. Originating in the mid-18th century through the works of Gottfried Achenwall, the field has undergone substantial methodological evolution. In atmospheric sciences, the integration of statistical reasoning into meteorology has given rise to meteorological statistics\u0026mdash;a specialized subfield concerned with the quantitative analysis of meteorological phenomena. These methods enable the interpretation of extensive observational datasets, facilitating the identification of patterns, anomalies, and long-term trends, which are critical for hypothesis testing, evaluating climate variability, and detecting anthropogenic signals in climate systems (Wilks \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; von Storch et al. 1999; Gong \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Della Marta 2008; Moore 2017; Mann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Triola \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Markovic \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Ivanovic 1976).\u003c/p\u003e \u003cp\u003eIn this study, the analysis began with the application of quartile statistics to determine seasonal thresholds, followed by the use of Student\u0026rsquo;s t-test and Fisher\u0026rsquo;s F-test to assess homogeneity and compare distinct temporal periods, thereby providing evidence for the anthropogenic character of recent climate changes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSeasonal determination based on temperature dynamics\u003c/h3\u003e\n\u003cp\u003eThe determination of seasons based on temperature dynamics requires a statistically robust methodology, which assumes that the reference period represents predominantly natural climate variability. In this study, the seasonal cycle is defined using quartile-based thresholds derived from a climatologically stable reference period (1951\u0026ndash;1980), assumed to reflect primarily natural atmospheric oscillations with minimal anthropogenic influence (Piotrowicz \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference period and statistical framework\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe reference period 1951\u0026ndash;1980 was selected due to its relative climatic stability and lower anthropogenic impact. Statistical parameters derived from this period serve as benchmarks for identifying seasonal transitions throughout the study period (1951\u0026ndash;2024). The primary statistical tool employed is the quartile position measure, which characterizes the distribution of mean daily temperatures for each calendar month. For the Prilep MMS, the quartile distribution of mean daily temperatures was calculated for each month of the reference period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These quartile values form the basis for defining temperature thresholds that delineate the beginning and end of each season.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuartile distribution of mean daily temperatures by month, Prilep municipality (1951\u0026ndash;1980).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(25%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian (50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(75%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJun\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDec\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;17.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eStatistical assessment of season duration\u003c/h3\u003e\n\u003cp\u003eThe homogeneity of season duration data was assessed by comparing two time periods: a previous period (1951\u0026ndash;1987) and a recent period (1988\u0026ndash;2024) using simple statistical methods. Student\u0026rsquo;s t-test and Fisher\u0026rsquo;s F-test were applied to investigate the relationship between sub-periods (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, 1987\u0026ndash;2005) and the recent period (2006\u0026ndash;2024), with the aim of determining whether statistically significant changes in seasonal duration distributions occurred. These analyses allowed for an empirical assessment of potential changes in climatic conditions attributable to anthropogenic influences.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStudent\u0026rsquo;s t-test: comparison of means\u003c/h2\u003e \u003cp\u003eTo assess changes in seasonal duration, a two-tailed Student\u0026rsquo;s t-test for means with unequal variances was employed (Student \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1908\u003c/span\u003e; Mann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Triola \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Markovic \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). This method is particularly suitable for detecting changes in continuous variables between two related time frames and is widely applied in environmental studies to evaluate the influence of external factors such as anthropogenic climate change. The results of the t-test are summarized in the Results section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFisher\u0026rsquo;s F-test: evaluation of homogeneity of variance\u003c/h2\u003e \u003cp\u003eComplementing the analysis of means, the Fisher F-test for equality of variances was used to determine whether the dispersion of seasonal durations differed significantly between the periods considered (Fisher \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1925\u003c/span\u003e; Mann \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Triola \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Markovic \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1973\u003c/span\u003e; Box \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1953\u003c/span\u003e; Montgomery et al. 2014; Wilks \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This test is crucial for assessing the stability of climate variability and identifying potential increases in the frequency or intensity of extreme events. Results of the F-test are presented in the Results section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAnalytical determination of seasons\u003c/h2\u003e \u003cp\u003eClassification of seasons is a fundamental component of regional climatological analysis (Trenberth \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), particularly in heterogeneous terrains such as North Macedonia, where Mediterranean, continental, and mountainous climates coexist across a wide altitudinal gradient (60\u0026ndash;1800 m a.s.l.) according to available meteorological stations. Given this climatic diversity, applying uniform seasonal thresholds based solely on mid-quartile (50%) temperature values is insufficient to capture local nuances (Wilks \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Contemporary research increasingly utilizes quantile-based methods or analyses of changes in seasonal onsets and terminations to improve precision (Zhang et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study adopts a geographically sensitive approach to defining annual seasons, focusing on the Prilep MMS (673 m a.s.l.), influenced by a continental\u0026ndash;sub-Mediterranean climate. Seasonal thresholds were obtained using a combination of long-term temperature averages from the reference period 1961\u0026ndash;1990 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and quartile distribution parameters calculated individually for each station (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSpring and winter thresholds\u003c/h2\u003e \u003cp\u003eConsidering transitional months according to the astronomical definition, the beginning of spring is in March. For stations at 60\u0026ndash;700 m a.s.l., the spring threshold was set at approximately 9\u0026deg;C (near the upper limit of observed March temperatures in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), corresponding to the 75th percentile of March temperatures (8.7\u0026deg;C) in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, reflecting the warmer end of early spring. For stations at 700\u0026ndash;1800 m a.s.l., thresholds were set near the upper limit of observed March temperatures (5\u0026ndash;6\u0026deg;C; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the winter threshold was defined using the 75th percentile of December temperatures, set at 5.3\u0026deg;C, while the 50th percentile was 2.2\u0026deg;C (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This adjustment accounts for variability across stations, aligning the threshold with the upper range of observed December temperatures (0.6\u0026ndash;4.9\u0026deg;C; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and ensuring more accurate detection of sustained cold conditions. For higher elevations (700\u0026ndash;1800 m a.s.l.), thresholds were close to the upper limit of observed December temperatures (-1.7\u0026ndash;3.3\u0026deg;C; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This methodology ensures that seasonal boundaries are adapted to the thermal realities of transitional months rather than being arbitrarily fixed, enhancing the climatological fidelity of seasonal classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSummer and Autumn Thresholds\u003c/h2\u003e \u003cp\u003eFor summer and autumn, the 50th percentile (median) of temperature values was considered adequate for determining seasonal thresholds. Based on Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the threshold for the onset of summer (June) was set at 19.6\u0026deg;C, which closely approximates the mean of observed June temperatures across stations (range: 17.2\u0026ndash;22.8\u0026deg;C; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage air temperatures North Macedonia (1961/90).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeteo. station\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eXII\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGevgelija\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValandovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDemir Kapija\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVeles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNov Dojran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrumica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkopje P.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmzabegovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKavadarci\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKatlanovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkopje R. Hill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eŠtip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKumanovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKočani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadoviš\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTetovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGostivar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMakedonski Brod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBitola\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKičevo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelčevo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKratovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrilep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDebar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKriva Palanka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStruga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOhrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBerovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKruševo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMavrovo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLazaropole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopova Šapka\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the onset of autumn (September), the threshold was set at 17.6\u0026deg;C, closely corresponding to the mean of observed September temperatures across stations (16.0\u0026ndash;21.0\u0026deg;C; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For stations at 700\u0026ndash;1800 m, thresholds were determined using the 50th percentile values from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This methodology ensures that seasonal boundaries are not arbitrarily fixed but are adapted to the thermal realities of transitional months, thereby improving the climatological fidelity of seasonal classification. These thresholds represent the average climatic conditions typical of the respective months and align with established climatological practices for defining seasonal transitions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal thresholds and detection criteria\u003c/h2\u003e \u003cp\u003eTo identify the beginning and end of each season in a given year, the following criteria were applied:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eA sequence of six consecutive days with mean daily temperatures above the respective seasonal threshold indicates the onset of spring, summer, or autumn.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA sequence of six consecutive days with temperatures below the winter threshold indicates the onset of winter.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe end of each season is defined as the day preceding the start of the next season, based on the same six-day rule.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis approach ensures that transient temperature fluctuations do not artificially alter seasonal boundaries and that only sustained thermal conditions define seasonal transitions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConsideration of transitional sequences\u003c/h2\u003e \u003cp\u003eIn cases where anomalous sequences lasting more than six days and falling outside the defined seasonal thresholds, occurred after the onset of a season, these sequences were flagged for further investigation. Although not analyzed in this study, such events may serve as indicators of seasonal variability and climate instability, warranting future research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal duration and normalization\u003c/h2\u003e \u003cp\u003eOnce the start and end dates of each season were determined, the total number of days comprising each season was calculated. To account for unequal lengths of calendar months and to facilitate interannual comparisons, average seasonal durations are expressed in per mille (\u0026permil;) of the total annual days, providing a normalized series for statistical analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eTemperature thresholds and seasonal duration\u003c/h2\u003e \u003cp\u003eTemperature thresholds for defining the onset of the four annual seasons were determined using the quartile distributions of mean daily air temperatures, calculated for the reference period 1951\u0026ndash;1980. The resulting thresholds for spring, summer, autumn, and winter are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. These values represent statistically derived transition points based on the thermal characteristics of the climatologically stable reference interval and form the basis for subsequent seasonal delineation.\u003c/p\u003e \u003cp\u003eUsing the seasonal detection methodology described in the preceding section, the duration of each season was computed for the full study period (1951\u0026ndash;2024). Seasonal lengths were derived from the quartile-based thresholds and expressed in per mille (\u0026permil;) of annual days, enabling direct comparison across years with differing seasonal patterns.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemperature thresholds according quartile distribution, MMS Prilep 1951\u0026ndash;1980.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature thresholds\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u0026deg;C (March)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.7\u0026deg;C (June)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.6\u0026deg;C (September)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.3\u0026deg;C (December)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results annualy are illustrated in Graphic 1 and Graphic 2. Spring duration exhibited considerable interannual variability, ranging from 100 to 295\u0026permil;, with both the maximum and minimum values occurring in the earlier historical periods. Summer duration ranged from 112 to 316\u0026permil;, with the longest summers recorded in the recent period and the shortest summers in the earlier periods. Autumn duration varied between 70 and 252\u0026permil;, displaying a similar pattern: the maximum in the recent period and the minimum in the historical periods. Winter duration ranged from 168 to 402\u0026permil;, with both extremes occurring in the more recent period, indicating an increased variability in winter length relative to the mid-20th-century baseline.\u003c/p\u003e \u003cp\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"2\" name=\"Chart 1\"\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGraph 1.\u003c/b\u003e Seasonal range in days by year, Prilep 1951\u0026ndash;2024 spring-summer, NHMS, Skopje.\u003c/p\u003e \u003cp\u003e \u003cdiv description=\"\" class=\"Drawing\" id=\"3\" name=\"Chart 2\"\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGraph 2.\u003c/b\u003e Seasonal range in days by year, Prilep 1951\u0026ndash;2024 autumn-winter, NHMS, Skopje.\u003c/p\u003e \u003cp\u003eThe range of seasonal durations for the period 1951\u0026ndash;2024 was expressed in per mille (\u0026permil;) of annual days to normalize interannual variability and to avoid distortions arising from unequal month lengths. This normalization enables a consistent comparison of seasonal extents across the full study interval. The mean seasonal durations for the two climatologically distinct periods\u0026mdash;1951\u0026ndash;1987 and 1988\u0026ndash;2024 are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage duration of seasons expressed in per mille (\u0026permil;).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951/1987\u003c/p\u003e \u003cp\u003e(\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1988/2024\u003c/p\u003e \u003cp\u003e(\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe deviation (contrast) between individual seasons was also quantified and expressed in per mille (\u0026permil;), with the results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContrast between the seasons Prilep, 1951\u0026ndash;2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeviation (\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeviation (\u0026permil;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpring/Autumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSummer/Winter\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1951/1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1988/2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, a comparative analysis of five-year mean seasonal contrasts was conducted for the entire 1951\u0026ndash;2025 period. These contrasts, also expressed in per mille (\u0026permil;), are illustrated in Graph 3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGraph 3.\u003c/b\u003e Five-year deviations between Spring and Autumn and between Summer and Winter for the period 1951\u0026ndash;2025 at the Prilep MMS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStudent\u0026rsquo;s t-test with unequal variances\u003c/h2\u003e \u003cp\u003eTo evaluate whether the duration of thermal seasons has undergone statistically significant shifts over time, Student\u0026rsquo;s t-tests with unequal variances (two-tailed) were applied to annual seasonal duration series from the Prilep MMS. Four historical intervals (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005) were each compared with the recent period (2006\u0026ndash;2024). This test, suitable for samples with heterogeneous variances, assesses whether the mean difference in seasonal length between two periods differs significantly from zero at the 95% confidence level (α\u0026thinsp;=\u0026thinsp;0.05). For each comparison, the degrees of freedom (df), t-statistic, p-value, critical t-value, and the 95% confidence interval for the mean difference (M₂ \u0026ndash; M₁) were computed (Student \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1908\u003c/span\u003e; Fisher \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1925\u003c/span\u003e; Box \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1953\u003c/span\u003e; Montgomery et al. 2014; Wilks \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marković 1973). The resulting statistics provide an empirical basis for detecting structural changes in seasonal duration and for interpreting their climatological significance.\u003c/p\u003e \u003cp\u003eThe results of the Student\u0026rsquo;s Welch t-test for spring at the Prilep MMS (NHMS, Skopje) indicate that there are no statistically significant differences in mean seasonal durations between the historical periods (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005) and the recent period (2006\u0026ndash;2024) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For example, the comparison between 1951\u0026ndash;1969 and 2006\u0026ndash;2024 yielded a t‑statistic (tstat) of 0.54, with a critical t‑value (tcrit) of 2.03. Since tstat\u0026thinsp;\u0026lt;\u0026thinsp;tcrit, the null hypothesis of equal mean seasonal durations is not rejected. The corresponding p-value of 0.59 indicates a 59% probability of accepting the null hypothesis, confirming that there is no statistically significant change in the length of spring between the earlier and recent period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudent\u0026rsquo;s t-test for Spring (comparison of periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 with 2006\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample assuming unequal variances (Spring)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u0026ndash;1969\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1969\u0026ndash;1987\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;1997\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [-8.76, 15.07]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [-9.37, 12.74]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [-8.24, 13.61]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [-6.92, 16.50]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the Student\u0026rsquo;s t-test for the summer period at the Prilep meteorological station (NHMS, Skopje) indicate that the comparisons of 1951\u0026ndash;1969 and 1987\u0026ndash;2005 versus 2006\u0026ndash;2024 show borderline statistically significant differences in mean seasonal durations. In contrast, the comparisons for 1969\u0026ndash;1987 and 1979\u0026ndash;1997 versus 2006\u0026ndash;2024 reveal statistically significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For the autumn period, the Student\u0026rsquo;s t-test results indicate no statistically significant differences in mean seasonal durations between the historical periods (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005) and the recent period (2006\u0026ndash;2024) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudent\u0026rsquo;s t-test for Summer (comparison of periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 with 2006\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample assuming unequal variances (Summer)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1951\u0026ndash;1969\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1969\u0026ndash;1987\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1979\u0026ndash;1997\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [-0.03, 21.39]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [5.37, 27.15]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [4.64, 29.58]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.62, 21.07]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudent\u0026rsquo;s t-test for Autumn, comparing the periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 with 2006\u0026ndash;2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample assuming unequal variances (Autumn)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u0026ndash;1969\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1969\u0026ndash;1987\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1979\u0026ndash;1997\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [-7.44, 10.91]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [-8.50, 12.28]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [-3.20, 16.99]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [-0.41, 18.30]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of the Student\u0026rsquo;s t-test for the winter period at the Prilep meteorological station (NHMS, Skopje) indicate that the comparisons of 1951\u0026ndash;1969, 1969\u0026ndash;1987, and 1987\u0026ndash;2005 versus 2006\u0026ndash;2024 show statistically significant differences in mean seasonal durations. In contrast, the comparison of 1979\u0026ndash;1997 versus 2006\u0026ndash;2024 reveals no statistically significant difference (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudent\u0026rsquo;s t-test for Winter, comparing the periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 with 2006\u0026ndash;2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample assuming unequal variances (Winter)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u0026ndash;1969\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1969\u0026ndash;1987\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;1997\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Stat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003et Critical two-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [1.58, 22.94]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [5.24, 29.9]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [-10.14, 23.19]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [10.50, 36.24]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eF-test two sample\u003c/h2\u003e \u003cp\u003eFisher\u0026rsquo;s F-test was applied to compare the variances of seasonal durations between the recent period (2006\u0026ndash;2024) and the preceding periods (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005) at the Prilep MMS (NHMS, Skopje). For spring, the F-values (1.05\u0026ndash;1.48) are below the critical F-value of 2.22 at the 95% confidence level, and the one-tailed p-values (0.21\u0026ndash;0.46) indicate no statistically significant differences in variance. The 95% confidence intervals for the variance ratios include unity, confirming that interannual variability in spring durations has remained largely stable over the analyzed periods (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFisher\u0026rsquo;s F-test for Spring, comparing the periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 with 2006\u0026ndash;2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample for variances (Spring)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1951\u0026ndash;1969 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1969\u0026ndash;1987 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1979\u0026ndash;1997 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2006\u0026ndash;2024\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(F\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;f) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.40, 2.72]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.57, 3.84]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.50, 3.38]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.44, 2.95]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFisher\u0026rsquo;s F-test for the summer period at the Prilep MMS (NHMS, Skopje) shows statistically significant differences in the variances of seasonal durations for 1951\u0026ndash;1969 and 1969\u0026ndash;1987 compared with 2006\u0026ndash;2024. For 1979\u0026ndash;1997 and 1987\u0026ndash;2005 versus 2006\u0026ndash;2024, the differences are borderline significant. The F-values (2.19\u0026ndash;2.76) exceed the critical F-value of 2.22, and the one-tailed p-values (0.02\u0026ndash;0.05) support these observations. The 95% confidence intervals for the variance ratios further reject the null hypothesis as true (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFisher-F test for Summer (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997 and 1987\u0026ndash;2005 vs. 2006\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample for variances (Summer)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u0026ndash;1969 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1969\u0026ndash;1987 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;1997 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(F\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;f) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [1.01, 6.80]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [1.06, 7.20]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.80, 5.70]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.90, 6.04]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFisher\u0026rsquo;s F-test for the autumn period at the Prilep MMS (NHMS, Skopje) indicates no statistically significant differences in the variances of seasonal durations for the periods 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 compared with 2006\u0026ndash;2024. The F-values (1.31\u0026ndash;1.94) are below the critical F-value of 2.22, and the one-tailed p-values (0.08\u0026ndash;0.29) confirm that the null hypothesis of equal variances is not rejected. The 95% confidence intervals for the variance ratios also include unity, supporting the absence of significant changes in interannual variability for autumn (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFisher-F test for Autumn (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997 and 1987\u0026ndash;2005 vs. 2006\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample for variances (Autumn)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1951\u0026ndash;1969 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1969\u0026ndash;1987 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1979\u0026ndash;1997 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(F\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;f) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.50, 3.40]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.75, 5.05]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.68, 4.64]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.54,3.64]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis of variances using Fisher\u0026rsquo;s F-test for the winter period at the Prilep MMS (NHMS, Skopje) demonstrates that the distributions of seasonal durations for 1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, and 1987\u0026ndash;2005 are statistically comparable with those of 2006\u0026ndash;2024. All F-values (1.1\u0026ndash;1.77) remain below the critical threshold of 2.22, and the corresponding one-tailed p-values (0.12\u0026ndash;0.42) indicate that the null hypothesis of equal variances is not rejected. Additionally, the 95% confidence intervals for the variance ratios encompass unity, confirming the stability of interannual variability in winter durations (Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFisher-F test for Winter (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997 and 1987\u0026ndash;2005 vs. 2006\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003et-Test: Two-sample for variances (Winter)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1951\u0026ndash;1969 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1969\u0026ndash;1987 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1979\u0026ndash;1997 Variable 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1987\u0026ndash;2005\u003c/p\u003e \u003cp\u003eVariable 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2006\u0026ndash;2024 Variable 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP(F\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;f) one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF Critical one-tail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.68, 4.60]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.42, 2.85]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.35, 2.35]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003eCI = [0.49, 3.33]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present analysis demonstrates pronounced changes in the duration of seasons at the Prilep MMS over the period 1951\u0026ndash;2024. Seasonal onsets and terminations were determined using temperature thresholds based on quartiles derived from a relatively stable pre-anthropogenic period (1951\u0026ndash;1980), providing an objective and station-specific definition of seasonal limits grounded in the statistical distribution of observed temperatures. This approach allows for temporal comparisons across different periods and altitudes and avoids the arbitrariness of fixed temperature thresholds. Its primary strengths lie in its adaptability and objectivity; however, it is sensitive to extreme values if not properly preprocessed and does not directly incorporate physiological or bioclimatic thresholds such as 0\u0026deg;C or 25\u0026deg;C.\u003c/p\u003e \u003cp\u003eAnalysis of seasonal duration indicates that summer has lengthened significantly over the examined periods, increasing from an average of 92\u0026ndash;98 days in earlier intervals to 109 days in the most recent period, with statistically significant differences confirmed by t-tests (t\u0026thinsp;=\u0026thinsp;2.04\u0026ndash;3.05; p\u0026thinsp;\u0026le;\u0026thinsp;0.05). In contrast, winter has contracted, decreasing from 117\u0026ndash;134 days to 110 days, with t-statistics exceeding critical values in multiple comparisons, indicating robust significance (t\u0026thinsp;=\u0026thinsp;2.34\u0026ndash;3.68; p\u0026thinsp;\u0026le;\u0026thinsp;0.03). Comparisons for spring and autumn reveal relative stability in duration, suggesting that the principal seasonal shifts are concentrated in the extreme summer and winter periods. Borderline significant results were observed in some summer comparisons between older intervals and the recent period, highlighting potential temporal variability within the transitional phases of warming.\u003c/p\u003e \u003cp\u003eVariance analysis further supports these findings. The F-test demonstrates that summer variance is significantly different between the pre-2006 periods and the most recent interval (F\u0026thinsp;=\u0026thinsp;2.19\u0026ndash;2.76; p\u0026thinsp;\u0026le;\u0026thinsp;0.05), indicating a trend toward increased stability in summer duration, while variance in other seasons does not exhibit statistically significant changes. Collectively, these results indicate an overall compression of winter duration and extension of summer, consistent with an increased homogeneity in the annual temperature cycle. Such patterns align with observations from broader European and Balkan studies, which report asymmetric seasonal changes associated with anthropogenic warming (Br\u0026aacute;zdil et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bissolli et al. 2001; Unkašević et al. 2013; Gavrilov et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Milevski et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tošić et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed extension of summer and contraction of winter is likely attributable to rising mean temperatures, which facilitate surpassing summer heat thresholds and hinder maintenance of winter cold thresholds. Spring remains relatively unchanged, likely due to the stabilizing influence of prevailing westerly storm systems, whereas autumn exhibits a modest contraction, indicative of prolonged summer warmth before the transition to cooler conditions. The differential response of inter-seasons, reflected in the increased fractional year difference between spring and autumn, further suggests that warming exerts seasonally heterogeneous effects. The overall narrowing of inter-seasonal differences, particularly between summer and winter, reflects a more uniform annual thermal cycle, which is consistent with climate warming scenarios reported across the region.\u003c/p\u003e \u003cp\u003eLong-term climatic drivers, including El Ni\u0026ntilde;o events and variability in the North Atlantic Oscillation, may have modulated these trends, particularly affecting the magnitude of summer\u0026ndash;winter and spring\u0026ndash;autumn contrasts (King et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Domeisen et al. 2025; Outten et al. 2024; Liu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Song et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While these teleconnections offer explanatory insights, anthropogenic forcing remains the most plausible driver of the observed seasonal shifts over the 70-year period, given the limited influence of orbital and axial variations on decadal timescales (Schneider et al. 1974).\u003c/p\u003e \u003cp\u003eLimitations of the study include reliance on a single station, which may not capture altitudinal and spatial heterogeneity across North Macedonia, ranging from low valleys to high mountain environments. While altitude-adjusted threshold rules have been proposed, their validity requires verification using multiple stations. Additionally, the six-day criterion for defining season onset, although operationally robust, may exclude short-term but climatically meaningful anomalies, leaving gaps in the characterization of extreme events such as prolonged heatwaves or dry spells, which have significant implications for agriculture and water management. To address these limitations, future research should incorporate data from multiple meteorological stations, supported by high-resolution reanalysis datasets such as ERA5, to evaluate the representativeness of observed seasonal changes and to refine temperature thresholds in relation to local climate variability.\u003c/p\u003e \u003cp\u003eFuture research should expand the spatial coverage to multiple meteorological stations and integrate high-resolution reanalysis datasets, enabling assessment of regional representativeness and refinement of altitude-specific temperature thresholds (Ananthu et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Paleo-climatic proxies such as dendrochronology or sediment records could elucidate longer-term variability and disentangle anthropogenic impacts from natural climate oscillations. Furthermore, numerical climate models may simulate seasonal evolution under different greenhouse gas emission scenarios, informing adaptation strategies in the context of projected warming (Aleksova et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Milevski et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Aleksova et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Milevski et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sabljić et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Automated statistical or machine learning approaches could facilitate dynamic adjustment of seasonal thresholds, enhancing monitoring accuracy and resilience planning. The study underscores the value of data-driven, temperature-based seasonal definitions over rigid calendar constructs, providing an empirical framework for early detection of climate-induced seasonal shifts and supporting climate adaptation measures.\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eConcluding remarks\u003c/h2\u003e \u003cp\u003eThis study is the first in North Macedonia to apply quartile-derived temperature thresholds for defining thermal seasons, providing empirical rigor and revealing a selective reconfiguration of the annual cycle at the Prilep MMS over seven decades. Normalized as per mille (\u0026permil;) fractions, these metrics circumvent calendar-based approaches and offer a template for phenological review. The greatest advantage of the method lies in its adaptability: by anchoring the thresholds to a previously forced baseline (1951\u0026ndash;1980), anthropogenic signals are isolated, offering practical insights into transitions that astronomical paradigms cannot capture. These findings hold major implications for regional climatology, as homogenized seasons can precipitate cascading vulnerabilities\u0026mdash;prolonged droughts threatening viticulture, unpredictable autumns disrupting harvest cycles, and weakened winters limiting snowpack recharge for water resources. In the heterogeneous terrain of North Macedonia, this framework elucidates altitudinal gradients in thermal partitioning, informing targeted adaptations that protect both agricultural yields and hydrological balances. Moreover, defining season lengths based on quartile temperature thresholds constitutes an additional metric for climate regionalization.\u003c/p\u003e \u003cp\u003eThe most common date in years when a particular temperature threshold is reached can be considered the general start of a given season. This approach enhances consistency in climate zone delineation by reflecting actual temperature dynamics rather than fixed calendar boundaries, allowing for improved alignment between climate classification and ecological or agricultural requirements (Tomczyk \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The applied method is based on quartiles that accommodate regions with varying temperature regimes and account for local context, although the choice of baseline periods exerts substantial influence, and thresholds may fluctuate over time. The singularity of the analysis constrains spatial generalization, and the lack of explicit examination of ENSO or NAO effects leaves external modulators underexplored, potentially confounding the attribution of observed variance. Despite these limitations, the study provides a foundational framework for further research, enabling the development of unambiguous links to radiative forcings and supporting evidence-based adaptation strategies to enhance resilience in climate-sensitive sectors.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eRecommendation\u003c/h2\u003e \u003cp\u003eThe findings for Prilep are consistent with regional trends in Southeast Europe, where summers are becoming longer and warmer, and winters are shorter and milder (Lelieveld et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Spinoni et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The observed reduction in variability during the recent period can be interpreted as a \u0026ldquo;temporary stabilization,\u0026rdquo; reflecting a transitional phase in the local atmospheric response to climate change. Should emissions and pollution continue to increase, the current quasi-stable balance may be disrupted, potentially inducing compensatory atmospheric cooling via thermoregulatory mechanisms. Such phenomena have also been observed in climate oscillation models (Stevenson et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), wherein the system exhibits delayed responses to excessive warming through self-regulating feedback processes over extended timescales. Accordingly, these observations underscore the necessity for proactive human engagement in maintaining thermal equilibrium through mitigation of pollution and enhancement of energy efficiency.\u003c/p\u003e \u003cp\u003eThe stabilization of future climate conditions will depend upon collective human commitment, technological integration, and regional ecological planning. Implementation of agroclimatic zoning, temperature-based seasonality metrics, sustainable land-use practices, and strategic vegetation management provides a concrete pathway for sustaining a balanced climate system and mitigating the risk of compensatory cooling over forthcoming decades. Modern technological tools, including artificial intelligence, offer potential for the management of extreme climatic events and for the optimization of agricultural activities, including planting, harvesting, transportation, and land-use planning, tailored to local climate and soil conditions (Hansen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Olesen et al. 2002). The deployment of agroclimatic indicators and thermal sums enables efficient regionalization of agricultural production and facilitates the selection of crops with maximal climatic suitability (FAO 2021).\u003c/p\u003e \u003cp\u003eThe broader implication of this research is that effective restoration and preservation of natural systems necessitate coordinated global action, leveraging both societal and technological capacities to enhance agricultural productivity, tourism, and ecosystem resilience. This approach embodies the principle of \u0026ldquo;solving nature with nature\u0026rdquo; (Sachs \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which can be further strengthened by integrated educational and hybrid knowledge strategies aimed at fostering sustainable environmental stewardship and ensuring climate-resilient regional development.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e: All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Lidija Stojova, Ivancho Kaevski and Bojana Aleksova. The first draft of the manuscript was written by Lidija Stojova, and the other authors (Bojana Aleksova and Ivancho Kaevski) commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Conduct\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is an observational study.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Lidija Stojova, Ivancho Kaevski and Bojana Aleksova. The first draft of the manuscript was written by Lidija Stojova, and the other authors (Bojana Aleksova and Ivancho Kaevski) commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAleksova B, Milevski I, Mijalov R, Marković S, Cvetković V, Lukić T (2024) Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index. 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Earth\u0026rsquo;s Future 12(7). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2024EF004697\u003c/span\u003e\u003cspan address=\"10.1029/2024EF004697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2024EF004697\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuo W, S\u0026aacute;nchez-Ben\u0026iacute;tez A, Athanase M, Jung T, Yao Y, Goessling HF (2025) Storylines reveal contrasting thermodynamic effects of climate change on 2020/21 East Asian cold extremes. npj Clim Atmospheric Sci 8 Article 169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41612-025-01031-x\u003c/span\u003e\u003cspan address=\"10.1038/s41612-025-01031-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraph 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Seasons, range, quartiles, Student T test, Fisher F test, anthropogenic climate change","lastPublishedDoi":"10.21203/rs.3.rs-9039534/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9039534/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study provides a meteorological‑statistical explanation of the way in which the range of seasons can be determined during the year, as well as the possibility of drawing conclusions related to climate change from their analysis. To the best of our knowledge, this is the first study in North Macedonia to analyze seasonal dynamics using such a statistical-meteorological approach. The study used data on daily temperature series and seasonal durations, obtained for the period 1951\u0026ndash;2024, from the main meteorological station in Prilep (National Hydrometeorological Service, NHMS‑Skopje, North Macedonia). The processing applied quartile measures of position, Student\u0026rsquo;s t‑test, and Fisher\u0026rsquo;s F‑test statistics. The range of seasons is defined by applying a quartile distribution of temperature. Furthermore, based on mathematical‑statistical analysis of the seasons, statistically significant differences were investigated between the previous (1951\u0026ndash;1969, 1969\u0026ndash;1987, 1979\u0026ndash;1997, 1987\u0026ndash;2005) and recent (2006\u0026ndash;2024) period. Significant differences in the mean values and variances of the summer range were observed. The increase in the mean values in the more recent period indicates an expansion of the seasonal range, while the decrease in variance reflects greater stability. In winter, increased emissions from heating and traffic created local thermal effects and statistical analysis showed a decrease in the winter range. Significant statistical differences indicate a structural change in seasonal dynamics. The cumulative effect of global warming, urbanization, and changes in the atmospheric circulation resulted in a more stable but warmer summer regime and shorter, warmer winters. These findings align with global evidence of a reduction in temperature variability coupled with an increase in the occurrence and severity of extreme heat events in recent decades (Zhou et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, the results demonstrate a shift toward a more thermally stable yet warmer climate regime, consistent with the impacts of contemporary anthropogenic climate change.\u003c/p\u003e","manuscriptTitle":"Determining seasonal ranges and climate change in North Macedonia (1951–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-15 14:10:00","doi":"10.21203/rs.3.rs-9039534/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e09f7ac5-be66-4f15-8926-ae0b3cf627a6","owner":[],"postedDate":"May 15th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T07:10:08+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"302550453891857027399827544484298798563","date":"2026-05-16T06:30:18+00:00","index":29,"fulltext":""},{"type":"reviewerAgreed","content":"288363294572046945057391806721314415216","date":"2026-05-12T19:36:24+00:00","index":28,"fulltext":""},{"type":"reviewerAgreed","content":"72917348903539427424889822066018573374","date":"2026-05-11T05:59:47+00:00","index":27,"fulltext":""},{"type":"reviewerAgreed","content":"267594224521300305633568829081060515767","date":"2026-05-09T14:03:53+00:00","index":26,"fulltext":""},{"type":"reviewerAgreed","content":"10832437016625388303856258118078429629","date":"2026-05-09T13:21:17+00:00","index":25,"fulltext":""},{"type":"reviewersInvited","content":"12","date":"2026-05-07T08:40:14+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T14:10:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-15 14:10:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9039534","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9039534","identity":"rs-9039534","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}