Contribution of Precipitation and Potential Evapotranspiration to Long-Term Changes in Aridity in Argentina Over Recent Decades

Contribution of Precipitation and Potential Evapotranspiration to Long-Term Changes in Aridity in Argentina Over Recent Decades | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Contribution of Precipitation and Potential Evapotranspiration to Long-Term Changes in Aridity in Argentina Over Recent Decades Pedro Samuel Blanco, Moira Evelina Doyle This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8781542/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Over recent decades, aridity has intensified in Argentina because of an imbalance between precipitation and atmospheric evaporative demand, yet the relative role of these factors in long-term changes remains poorly explored. This study analyses precipitation (PRE) and potential evapotranspiration (PET) contributions to aridity changes across Argentina during 1961–2020. Using monthly precipitation and mean temperature data from the Climatic Research Unit (CRU), the United Nations Environment Programme (UNEP) aridity index (AI) was calculated at annual and seasonal scales. Subsequently, linear and nonlinear trends, climatic shifts, and PRE and PET contributions to AI changes were evaluated for six regions using fixed 30-year periods and pre- and post-shift means. The results show a widespread decrease in annual AI across much of Argentina (1–10% per decade), driven primarily by PRE reductions and, to a lesser extent, by PET increases. Seasonally, AI increases prevail in summer and autumn (locally > 30% per decade), decreases in winter (1–30% per decade), and a mixed pattern emerges in spring (-20 to 10% per decade), with PRE as the dominant control. Nonlinear variability in the AI is controlled by PRE, with the strongest declines observed in the Andean and Extra-Andean Patagonia (-2.9 and − 2.3% per decade), associated with PRE decreases and PET increases. In contrast, the Northwest and Subandes regions show the greatest seasonal variability, with summer AI increases (7–16% per decade) and abrupt shifts (70–83%), whereas in East PET increases (> 3.4%) exceed the PRE contribution and account for more moderate AI declines (-2.5%). Overall, aridification in Argentina is primarily governed by PRE changes, while PET acts as a regional and seasonal amplifier, highlighting the need for an integrated water-balance approach to assess aridity and its impacts. Aridity Index Linear Trend Polynomial Regression Climatic Shifts Aridification Regions Argentine Republic Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION In recent decades, climate change has significantly altered the global hydrological cycle, manifesting in changes in precipitation regimes, a sustained increase in evaporation and evapotranspiration, a higher occurrence of extreme hydrometeorological events such as droughts and floods, and a reduction in mean streamflow across extensive arid and semiarid regions (Kundzewicz, 2008 ; Tang, 2020 ; Rivera et al., 2021 ; Wang and Liu, 2023 ). As a result, a persistent increase in aridity has been documented in various regions worldwide, including northern Russia, the Gobi and Taklamakan deserts, eastern China, the Arabian Peninsula, western and eastern Africa, northwestern Canada, Patagonia, among others (Prăvălie et al., 2019 ; IPCC, 2021 ; Ullah et al., 2022 ). This process, known as aridification, represents a growing threat to ecosystems and human societies, as it intensifies the frequency and severity of droughts, reduces surface and groundwater availability, and negatively affects biological productivity and ecosystem services (Greve et al., 2019 ; Grünig et al., 2023 ; Berdugo et al., 2020 ; Williams et al., 2020 ). In this context, the study of aridity across different spatial and temporal scales is crucial to understanding its dynamics, assessing its impacts, and providing a scientific basis for the sustainable management of water and land resources. A fundamental characteristic of aridity is the persistent scarcity of water which, in terms of the water balance, occurs when precipitation inputs are lower than the atmospheric evaporative demand, usually represented by evapotranspiration (Nicholson, 2011 ; Cherlet et al., 2018 ). This hydroclimatic condition imposes strong constraints on ecosystems and productive activities, which must adapt to limited water availability on a seasonal or permanent basis (Huang et al., 2017 ; Koutroulis et al., 2019; Moreno-Jiménez et al., 2019 ). In this sense, increasing aridity in regions with low water availability intensifies environmental and socioeconomic vulnerability, affecting nearly 40% of the world’s population and exacerbating issues such as declines in agricultural production, restrictions on access to water, and soil degradation associated with desertification processes (Moral et al., 2016 ; Pellicone et al., 2019 ; Sardans et al., 2024 ). Consequently, identifying and quantifying changes in aridity, as well as their underlying climatic components, is essential for proper risk assessment and the design of adaptation strategies in regions highly dependent on water resources. Aridity is a multidimensional phenomenon resulting from the interaction of atmospheric, edaphic, hydrological, ecological, and human factors (Ullah et al., 2022 ). Depending on these factors, different types of aridity can be distinguished (Lian et al., 2021 ): atmospheric aridity (greater atmospheric water demand relative to precipitation inputs), edaphic or agricultural aridity (soil water stress), hydrological aridity (deficits in surface runoff), and ecological aridity (insufficient moisture to sustain vegetation growth). In the context of climate change, atmospheric aridity has received increasing attention, as rising air temperatures have intensified atmospheric evaporative demand and altered precipitation patterns across large regions of the world, with projections indicating a further intensification of these conditions in the future (Huang et al., 2016 ; Koutroulis et al., 2019; Moreno-Jiménez et al., 2019 ; Chai et al., 2021 ; Luo et al., 2023 ). This scenario has motivated numerous studies to examine not only observed changes in aridity using indices that quantify it but also trends in the climatic variables that control it, such as precipitation and temperature (Li et al., 2017 ; Liu et al., 2019 ). Accordingly, many studies have adopted water-balance-based approaches that consider precipitation as the input variable and potential evapotranspiration (PET), estimated from air temperature, as the output variable. By relying on widely available data and reproducible methods, these approaches make it possible to characterize changes in aridity and to assess how precipitation and PET contribute to those changes (Feng and Fu, 2013 ; Pan et al., 2021 ; Carvalho et al., 2022 ; Zhang et al., 2022 ). Nevertheless, global-scale studies that jointly analyse the evolution of aridity and the relative contribution of these climatic variables remain scarce (Feng and Fu, 2013 ; Fu and Feng, 2014 ; Lin et al., 2016 ). Several global studies have analysed historical and projected trends in aridity and have attributed these changes mainly to precipitation and PET. An expansion of arid and semiarid regions has been documented over recent decades, associated with increasing PET and heterogeneous changes in precipitation (Feng and Fu, 2013 ). Consistently, a recent intensification of aridity has been observed in global drylands, along with drying trends in humid and subhumid regions, resulting from the combined effects of precipitation and PET (Daramola and Xu, 2022 ; Luo et al., 2023 ). Along similar lines, increases in PET have been identified as a key contributor to the rise in global droughts, although regional variations in drought duration and intensity are strongly modulated by precipitation (Wang et al., 2022 ). It has also been shown that precipitation explains most of the changes in aridity across large portions of the world’s drylands, while warming has steadily increased the relative contribution of PET (Yang et al., 2019 ; Pan et al., 2021 ). Nevertheless, these studies tend to emphasize aggregated patterns and do not examine how the relative contribution of both drivers varies at regional or national scales or how it evolves over time, which limits the identification of dominant mechanisms in climatically heterogeneous regions. In Argentina, some studies have reported significant changes in aridity over recent decades. At the national scale, increasing aridity has been observed across extensive regions, particularly along the western mountainous belt (Blanco and Doyle, 2024 ), while opposite signals have been reported in the southwestern Pampean Plains, where a retreat of arid and semiarid areas has been mainly associated with changes in precipitation (Casañas et al., 2024 ). In central regions, increased precipitation during the twentieth century temporarily reduced aridity, although accompanied by greater seasonality and rainfall concentration (Rusticucci and Penalba, 2000 ; Minetti et al., 2003 ; Penalba and Vargas, 2004 ; De la Casa and Ovando, 2014 ; Barros et al., 2015 ; Doyle, 2020 ). In parallel, warming and the intensification of thermal extremes have increased atmospheric water demand, particularly in the Pampean and Patagonian regions (Barros et al., 2015 ; Ferrelli et al., 2021 ; Müller et al., 2021 ; Brendel et al., 2025 ), so that even in areas with higher precipitation, aridity may intensify due to rising PET and the recurrence of dry periods (Llano and Penalba, 2011 ; Rivera et al., 2013 ; Panza et al., 2025 ). Regional studies further indicate a recent transition toward warmer and drier conditions in northern Patagonia and a reduction in water resources in the Andes, associated with lower precipitation and higher evaporative demand (Hurtado et al., 2023 ; Rivera et al., 2021 ). Overall, although these studies describe patterns of aridity change and their associated climatic variables, there remains a lack of integrated analyses that examine long-term aridity evolution at the national scale and explicitly quantify the relative contribution of precipitation and PET. The objective of this study is to analyse the contribution of precipitation and PET to long-term changes in aridity in Argentina during the 1961–2020 period. To this end, long-term variations—including trends and significant shifts—in aridity and its associated climatic variables are evaluated, and the relative contribution of precipitation and PET to total aridity change is quantified. Given Argentina’s large territorial extent and pronounced climatic diversity (Beck et al., 2018 ), the analysis is conducted at both national and regional scales, allowing the integration of countrywide aridity evolution with a more detailed examination of regional patterns and the climatic factors that explain them. 2. DATA AND METHODS 2.1. Data and Aridity Index The monthly air temperature and precipitation data for the period 1961–2020 used, were obtained from version 4.06 of the Climatic Research Unit (CRU) dataset. This dataset is constructed by interpolating meteorological station observations using the angular-distance weighting method on a 0.5° regular grid (Harris et al., 2020 ) and is available for all continental areas except Antarctica. For this study, data were extracted for continental Argentina, located in southern South America (Fig. 1 a). To achieve greater detail in the analysis of spatial patterns of aridity and its associated variables, the data were resampled to a 0.16° regular grid using bilinear interpolation. This approach has been widely applied in downscaling studies and in the evaluation of global climate models, showing better performance than other interpolation methods and allowing a more detailed spatial analysis of regional climate variability (Peng et al., 2019 ; Zazulie et al., 2017 ; Rivera and Arnould, 2020 ). The UNEP aridity index (1997) is defined as the ratio between precipitation (PRE [mm]) and potential evapotranspiration (PET [mm]) (Eq. 1 ). PET was estimated using the Thornthwaite ( 1948 ) method, which converts mean monthly air temperature into a “heat index” representing the energetic efficiency of temperature in generating evapotranspiration. $$\:AI=\frac{PRE}{PET}$$ 1 The AI was calculated at annual and seasonal scales (summer [DJF], autumn [MAM], winter [JJA], and spring [SON]). Its values can be classified into different climate types. Hyperarid (0-0.05) and arid (0.05–0.2) climates are characterized by very low precipitation, insufficient to meet atmospheric demand. Semiarid (0.2–0.5) and dry subhumid (0.5–0.65) climates also experience water deficits, though with lower intensity. Wet subhumid (0.65-1) and humid (> 1) climates exhibit water availability during most or all the year. 2.2. Study Area and Regionalisation The study area corresponds to the Argentina continental (2,791,810 km 2 ), located at the southern tip of South America (Fig. 1 a). This territory is divided into three major topographic units that condition the regional climate (Pereyra, 2019 ): the Andes Mountain Range (western sector), the Chaco-Pampean Plains (eastern sector), and the Patagonian Plateau (south of 40°S). The Andes form an extensive mountain range with marked contrasts between peaks and valleys and are subdivided into the Puna (3,000–5,000 m), the Frontal Andes (4,000–6,000 m), the Principal Andes (1,000–4,000 m), and the Patagonian Andes (1,000–3,000 m). This system also includes lower-altitude ranges such as the Sub-Andean Ranges and the Pampean Ranges, around 65°W. The Chaco-Pampean Plains comprises the Chaco Plains (north) and the Pampean Plains (south), with elevations below 200 m, the Argentine Mesopotamia between the Paraná and Uruguay rivers, and, in its northeastern extreme, the Misiones Plateau, with elevations between 500 and 900 m. Finally, the Patagonian Plateau encompasses plateaus and basins with elevations ranging from 200 to 1,500 m. Considering Argentina’s topographic diversity, a regionalization of the country was performed using multiple criteria. First, the mean annual AI field (average for the 1961–2020 period) was used, allowing the identification of homogeneous climatic structures (Fig. 1 b). Second, the topographic field was superimposed on the mean annual AI, which made it possible to distinguish regions sharing the same climatic category but differing in topographic characteristics. Finally, geographic location was evaluated to determine whether regions with the same AI category were contiguous or separated by areas with other climate types. Based on these criteria, six regions were defined (Fig. 1 c): Northwest (predominantly semiarid climate, encompassing the Puna and the Frontal Andes); Subandes (subhumid climate, over the Sub-Andean Ranges); Transition (subhumid climate, in the intermediate zone between the Chaco-Pampean Plains and the eastern Sub-Andean Ranges, the Pampean Ranges, and the northeastern Patagonian Plateau); East (humid climate, over the eastern Chaco-Pampean Plains, Argentine Mesopotamia, and the Misiones Plateau); Andean Patagonia (humid climate, in the Principal and Patagonian Andes); and Extra-Andean Patagonia (semiarid climate with some arid areas, mainly over the Patagonian Plateau). Annual and seasonal AI values were calculated and spatially averaged for each defined region over the 1961–2020 period. A one-way analysis of variance (ANOVA) (Corobov and Overcenco, 2007 ; Da Silva et al., 2017 ) was then applied to the annual AI data, using Fisher’s F test to determine whether regional means differed significantly at the 95% confidence level. The ANOVA was also applied to seasonal AI data (not shown), yielding results like those obtained using annual AI. To identify which regions differed specifically, Tukey’s Multiple Comparisons test (Benjamini and Braun, 2002 ; Corobov and Overcenco; 2007 ) was used, allowing the grouping of distributions with similar means and the detection of statistically homogeneous groups. The Fisher’s F test applied to the annual AI series indicate that at least one regional mean differs significantly from the others at the 95% confidence level (p-value < 2 × 10 − 16 ). Tukey’s test (Table 1 ) shows that the East and Andean Patagonia regions (Group A) have statistically similar means; the Subandes and Transition regions (Group B) form a distinct group; and the Northwest (Group C) and Extra-Andean Patagonia (Group D) regions differ statistically from each other and from the remaining groups. It is worth noting that although the Subandes and Transition regions form a single group, they differ in their geographic characteristics: the Subandes are characterized by the complex topography of the Sub-Andean Ranges, whereas the Transition region is distinguished by the change in slope between the Chaco-Pampean Plains and the mountainous areas of western Argentina. Similarly, the East and Andean Patagonia regions also differ in terms of their location and contrasting topographies (plains in the East and mountains in Andean Patagonia). Table 1 Groups identified using Tukey’s test Region Average annual AI Group Northwest 0.44 C Subandes 0.73 B Transition 0.72 B East 1.22 A Andean Patagonia 1.20 A Extra-Andean Patagonia 0.26 D 2.3. Analysis of Linear and Non-linear Trends The linear trend was estimated from the slope of a simple linear regression. The magnitude of this slope, expressed in units of the variable per year or per decade (by multiplying by 10), quantifies the rate of change of the variable. A positive value indicates an increase in the variable, whereas a negative value reflects a decrease. To compare rates of change among variables with different units, the relative rate of change with respect to the climatic mean (B) can be used, as defined by Eq. 2 : $$\:B=\frac{\beta\:}{\stackrel{-}{X}}\times\:100$$ 2 where β is the slope of the linear regression line and X̅ is the climatic mean of the variable for the 1961–2020 period. The statistical significance of the slope was assessed using Student’s t test at the 95% confidence level. The nonlinear trend was estimated using third-order polynomial regression. The significance of the parameters was evaluated with Student’s t test, and the overall significance of the model was verified using Fisher’s F test, both at the 95% confidence level. The goodness of fit was assessed through the adjusted coefficient of determination (adj-R 2 ), which indicates the proportion of data variability explained by the model, adjusted to penalize the inclusion of irrelevant variables. An adj-R 2 close to 1 suggests an excellent fit, whereas low or negative values indicate that the model does not adequately explain the variability. Maps of linear (B) and nonlinear (adj-R 2 ) trends were produced for the study area and for the analysed climatic variables (AI, PRE, and PET), at both annual and seasonal scales. In addition, these trends were calculated for the spatially averaged time series corresponding to the defined regions of the country. 2.4. Detection of Climatic Shifts To detect climatic shifts in series, the classical Hubert segmentation procedure was applied (Hubert and Carbonnel, 1987 ; Hubert et al., 1989 ). This nonparametric technique allows the identification of one or several abrupt and significant changes in the temporal mean of a series, without requiring a reference series. The procedure consists of dividing the series into segments whose means differ significantly, based on an optimal least-squares segmentation using a step-by-step combinatorial approach (Hubert, 2000 ; Jun and Bantin, 2017 ; Hadour et al., 2020 ). To assess the robustness of the segmentation, Scheffé’s test (Scheffé, 1953 ) was employed, which determines the statistical significance of differences in means between contiguous segments (Diallo and Knudby, 2023 ). Assuming stationarity of the series, this test allows identification of whether the generated segments differ significantly at the 95% confidence level. Hubert segmentation was applied to the annual and seasonal AI series to detect abrupt changes in the hydroclimatic conditions of the country’s regions. The same analysis was performed for PRE and PET to evaluate whether discontinuities in the AI resulted from variations in one or both variables. A positive shift indicates an increase in the AI, PRE, or PET, whereas a negative shift indicates a decrease. Each identified climatic shift was characterized by its type (positive or negative), timing, regions of occurrence, and the associated percentage change. The percentage change (Δ%) was calculated from the mean values of the variables before and after the year of the climatic shift following Eq. 3 : $$\:{\Delta\:}\%=\frac{{X}_{t}-{X}_{t-1}}{{X}_{t-1}}\times\:100$$ 3 where X t is the mean AI, PRE, or PET after the shift and X t−1 is the mean before the shift. For example, if a shift occurred between 1980 and 1981 within the 1961–2020 period, X t corresponds to the mean for 1981–2020 and X t−1 to the mean for 1961–1980. 2.5. Estimation of the Contributions of PRE and PET to Changes in the AI Changes in the AI are attributed to changes in the climatic variables involved in its calculation. The relative contribution of PRE and PET to changes in the AI was quantified using the equation proposed by Feng and Fu ( 2013 ), based on a second-order Taylor polynomial expansion (Eq. 4 ): $$\:\varDelta\:\left(\frac{PRE}{PET}\right)=\frac{1}{{PET}_{1}}\varDelta\:PRE-\frac{{PRE}_{1}}{{\left({PET}_{1}\right)}^{2}}\varDelta\:PET+\frac{{PRE}_{1}}{{\left({PET}_{1}\right)}^{3}}{\left(\varDelta\:PET\right)}^{2}$$ 4 The left-hand side of the equation represents the change in the AI, calculated as the difference between the mean values of two defined subperiods. The first term on the right-hand side represents the relative contribution of PRE, assuming PET remains constant at the mean of the first subperiod (subscript 1), while the second and third terms indicate the relative contribution of PET, with PRE held constant. By multiplying each term by 100, these contributions can be expressed as percentages of the total change in the AI. For this study, a climatological analysis was conducted by dividing the study period into two subperiods of equal length (1961–1990 and 1991–2020), and changes were evaluated at both annual and seasonal scales. In this case, PRE 1 and PET 1 correspond to the mean values of the first subperiod. In addition, the same methodology was applied using subperiods defined by the shifts identified in the AI series, calculating differences between the period after the shift and the period before it. Only shifts identified in the AI series were considered, as the aim was to evaluate how PRE and PET respond during intervals when the index itself undergoes significant variations, thereby linking temporal boundaries directly to aridity changes and allowing the PRE and PET contributions to be interpreted as drivers of the observed AI shifts rather than as independent climatic transitions. Maps were produced showing the relative contribution of PRE and PET and the change in the AI, considering both the climatological subperiods and those defined by the AI shifts. In addition, graphs of AI change and the contributions of PRE and PET were constructed, spatially averaged for the defined regions of the country, applying the same subperiod criteria. The statistical significance of AI changes and the contributions of PRE and PET was assessed using Student’s t test at the 95% confidence level. 3. RESULTS 3.1. Spatial Distribution of Linear and Non-linear Trends Figure 2 shows the spatial distribution of linear trends in the AI and its associated climatic variables. These trends are expressed as percentage per decade relative to the temporal mean of the 1961–2020 period. This approach allows intercomparison among variables and helps identify which one acts as the main driver of aridity changes in each region. The annual AI exhibits both negative and positive trend areas across Argentina (Fig. 2 a). Negative trends (0–10% per decade) prevail over much of the territory and are more pronounced in the mountainous regions of the west (35–40°S). Statistically significant decreases in annual AI are in the Andean region, central Argentina, isolated sectors of the Puna, and the central Patagonian Plateau. In contrast, positive trends (0–5% per decade) are concentrated in southern Mesopotamia, the southwestern Pampean Plains (37°-41°S and 63°-66°W), and the southernmost part of Patagonia. AI trends vary seasonally across different regions of Argentina (Figs. 2 b-e). In summer, positive trends dominate the north, northwest, and central regions, with significant maxima in the Puna and the Sub-Andean Ranges (10–20% per decade), while significant negative trends are recorded in the Patagonian and Principal Andes (10–15% per decade). In autumn, the pattern reverses, with significant increases in the AI in the Principal and Patagonian Andes (10–15% per decade) and decreases of similar magnitude in the Puna. Winter is characterized by a predominance of negative trends, more intense in central and northern regions (5–15% per decade, significant in the Sub-Andean Ranges), whereas slight increases (0–5%) are observed in the Puna, the Frontal Andes, northern Mesopotamia, the central Patagonian Plateau, and some coastal areas. In spring, negative trends are concentrated west of 65°W (0–15% per decade, significant in the Andes between 30° and 40°S), while positive but non-significant trends (0–5% per decade) are identified to the east. Precipitation exhibits spatial patterns of linear trends like those of AI (Figs. 2 f-j). Annual PRE shows a negative trend (0–5%), more pronounced and significant in the central Andes (5–10%), while positive but non-significant trends are observed east of the Pampean Plains and in the southernmost part of the country (0–5%). In summer, increases dominate, especially in the northwest (10–20%, significant), in contrast to decreases in the Principal and Patagonian Andes (1–15%, significant). During autumn, the central and northern regions show reductions (5–15%, significant), whereas the Patagonian Plateau and the Patagonian Andes exhibit increases (5–15%, significant in the Principal and Patagonian Andes and in the northern plateau). In winter, negative trends prevail, being stronger and significant east of the Sub-Andean Ranges, in central Argentina, and in the northern Chaco Plains (10–20%), while in some areas slight non-significant precipitation increases (0–10%) are observed. Finally, in spring, significant increases are identified in the central and eastern regions (0–5%), whereas the west and south display decreases (5–15%, significant in the Frontal and Principal Andes). Annual PET (Fig. 2 k) shows a predominance of slight but significant positive trends (0–1%), although these vary by season. In summer (Fig. 2 l), slight PET increases (0–5%) are observed, significant in central Argentina and Patagonia, whereas in autumn and winter (Figs. 2 m-n) mixed patterns of increases and decreases occur, without statistical significance. During spring (Fig. 2 o), negative trends appear in the south (0–1%), while positive trends occur in central and northern Argentina (0–5%), significant north of 35°S. Linear trend patterns of AI are mainly modulated by precipitation trends. Nevertheless, PET can either moderate or intensify these patterns. For instance, the region exhibiting a negative AI trend is more extensive than that of precipitation alone, because the positive PET trend amplifies the effect of decreasing precipitation, extending arid conditions into areas where precipitation by itself would not show such a pronounced decline. Figure 3 displays the spatial distribution of the adjusted coefficient of determination (adj-R 2 ) from the third-order polynomial regression for the AI and its associated climatic variables at annual and seasonal scales. The annual AI (Fig. 3 a) shows high and significant adj-R 2 values (0.2–0.4) in the northwest and in the Principal Andes, indicating good performance of the polynomial fit. In contrast, low or negative values (below 0.1) are observed in the eastern, central, and southern parts of the country, indicating poor nonlinear fit. In summer (Fig. 3 b), the central and northwestern regions stand out with significant values above 0.4, while the rest of the country exhibits low or negative values. In autumn (Fig. 3 c), the Puna and the Principal and Patagonian Andes reach high and significant values, unlike the Patagonian Plateau and the central-eastern regions, where lower and non-significant values prevail. In winter (Fig. 3 d), a continuous band from the extreme northwest to Patagonia shows high and significant adj-R 2 values, whereas eastern Argentina and some Andean sectors exhibit low, non-significant values. In spring (Fig. 3 e), the Puna, northeastern Argentina, and the Frontal and Principal Andes display high and significant values. Precipitation shows adj-R 2 patterns that in several cases resemble those of the AI, although with some differences. At the annual scale (Fig. 3 f), patterns are like those of AI, with significant values in the northwest and in the Principal Andes. In summer (Fig. 3 g), much of northern and central Argentina, together with a belt over the Patagonian Andes and the southern plateau, exhibits high and significant values above 0.4. In autumn (Fig. 3 h), high values extend over large areas of the country and include sectors of central and northern Patagonia. In winter (Fig. 3 i), low or negative values dominate, although some central and northern areas east of the Andes show relatively high and significant values (0.25–0.40). In spring (Fig. 3 j), high and significant values are recorded in the Puna, the Andean belt, and northeastern Argentina, while lower values occur in central Argentina, the Pampean region, and Patagonia. Regarding PET, at the annual scale (Fig. 3 k) positive and significant values (greater than 0.2) prevail, with maxima along the Andes between 30° and 40°S and in the eastern Pampean Plains. In summer and spring (Figs. 3 l and 3 o), positive values are again widespread, concentrating in the central-western regions and Patagonia during summer, and in the northwest and northern Chaco Plains during spring. In autumn and winter (Figs. 3 m and 3 n), negative values dominate, indicating that the polynomial model performs worse than an estimate based solely on the mean, and that the nonlinear structure explains only a limited portion of the series variability. The nonlinear dynamics of the AI are mainly driven by precipitation, which exhibits the highest and most consistent adj-R 2 values across different regions and seasons. Evapotranspiration also contributes, though more modestly, with significant responses concentrated in the Andean region, the central-west, and the eastern Pampean Plains. Overall, both variables display spatial patterns consistent with those of the AI, particularly in the northwest, the Puna, and the Andes, where the nonlinear signal remains more stable at the annual scale and during summer and autumn. 3.3. Long-term Changes in the AI Long-term changes in the AI for the 1961–2020 period were analysed by estimating linear trends (simple regression), non-linear trends (third-order polynomial regression), and climatic shifts (Hubert segmentation) in annually and seasonally averaged time series for the defined regions of the country. Figure 4 shows the annual and seasonal AI series for the six regions, together with the linear regression line, the polynomial curve, and the significant shifts detected. In addition, Tables 2 and 3 show, respectively, the linear trends (B) and the adjusted coefficient of determination (adj-R 2 ) values of the annual and seasonal series of AI and its associated climatic variables for each region of the country. Table 2 Annual and seasonal linear trends of the aridity index (AI), precipitation (PRE), and potential evapotranspiration (PET) for the different regions of the country. Values are expressed as the percentage of the slope relative to the mean of the 1961–2020 period per decade. Asterisks indicate statistically significant linear trends, assessed using the Student’s t-test at the 95% confidence level (p-value < 0.05) Regions Variables Annual Summer Autumn Winter Spring Northwest AI -2.78 14.09* -1.88 -1.73 -5.75 PRE -2.21 13.68* -10.09* 3.31 -4.16* PET 0.69* 0.86* 0.14 0.15 1.09* Subandes AI -1.98 15.51* -4.97 -8.07 0.76 PRE -1.48 16.13* -10.98* -9.03* -0.34 PET 0.66* 0.78* 0.08 0.20 1.17* Transition AI -1.56 6.98* 0.44 -7.06* 1.22 PRE -1.03 8.24* -4.78* -4.44 2.31 PET 0.63* 0.72* 0.15 -0.16 1.12* East AI -0.45 3.21 3.12 -3.96 2.81 PRE 0.02 4.36* -0.94 -1.75 2.93 PET 0.73* 0.63 0.44 0.13 1.33* Andean Patagonia AI -2.88* -4.81 9.45* -3.29 -5.19 PRE -2.48* -4.92* 8.09* 0.13 -4.71* PET 0.53* 0.72* 0.38 2.27 0.05 Extra-Andean Patagonia AI -2.25* 3.00 7.76* -6.14 -3.45 PRE -1.75 3.10 5.14* -1.62 -2.47 PET 0.60* 1.02* 0.31 0.65 0.11 Table 3 Adjusted coefficient of determination (adj-R 2 ) of the third-order polynomial model for the annual and seasonal aridity index (AI), precipitation (PRE), and potential evapotranspiration (PET) for the different regions of the country. Asterisks indicate statistically significant polynomial models, assessed using the Fisher test at the 95% confidence level (p-value < 0.05) Regions Variables Annual Summer Autumn Winter Spring Northwest AI 0.13* 0.35* 0.11 -0.02 0.05* PRE 0.13 0.62* 0.29* 0.02 0.14* PET 0.19* 0.06* -0.05 -0.02 0.04* Subandes AI 0.13 0.40* 0.08 0.17 0.01 PRE 0.10 0.60* 0.36* 0.16* 0.08 PET 0.19* 0.07* -0.05 -0.03 0.12* Transition AI 0.08 0.13* 0.03 0.06* -0.01 PRE 0.05 0.48* 0.27* 0.02 0.12 PET 0.21* 0.07* -0.05 -0.05 0.10* East AI -0.02 0.05 0.00 0.01 0.00 PRE -0.03 0.17* 0.06 -0.03 0.12 PET 0.31* 0.01 -0.03 -0.05 0.13* Andean Patagonia AI 0.09* -0.01 0.08* -0.02 0.02 PRE 0.07* 0.10* 0.42* -0.01 0.17* PET 0.24* 0.09* 0.02 -0.03 -0.05 Extra-Andean Patagonia AI 0.06 -0.02 0.03* 0.03 -0.02 PRE 0.06 0.02 0.17* 0.03 -0.02 PET 0.28* 0.16* 0.01 -0.05 -0.05 The annual AI (Fig. 4 a) exhibits a negative linear trend in all regions, being statistically significant in Andean and Extra-Andean Patagonia, with decreases of 2.88% and 2.25% per decade, respectively. The East region shows a smaller, non-significant reduction in the AI (0.45% per decade), likely influenced by subregional areas with positive trends that dampen the regional mean. A similar pattern is observed in the Transition region, where the non-significant negative trend (1.56% per decade) is modulated by areas with opposing signals within the region. Non-linear trends in annual AI show, for the Northwest, Subandes, Transition, and Extra-Andean Patagonia, a rapid increase until the early 1980s, followed by a sustained and significant decrease, whereas in Andean Patagonia and the East region a polynomial fit like the linear trend is observed. At the seasonal scale, regions show coherent patterns. In summer (Fig. 4 b), positive trends dominate and are significant in the Northwest, Subandes, and Transition regions (6.98–15.51% per decade), while Andean Patagonia exhibits a non-significant negative trend. In autumn (Fig. 4 c), positive trends occur in the Transition, East, and Patagonian regions (both Andean and Extra-Andean), being significant in the latter two and with the largest increase in Andean Patagonia (9.45% per decade), whereas non-significant negative trends are found in the Northwest and Subandes regions. In winter (Fig. 4 d), a negative trend is observed nationwide, although it is statistically significant only in the Transition region (7.06% per decade). In spring (Fig. 4 e), the index decreases in the Northwest and Patagonia (Andean and Extra-Andean) and increases in the rest of the country, without statistical significance. Seasonal non-linear trends exhibit low-frequency oscillations superimposed on linear changes. In summer, a decrease prior to 1990 is followed by a marked increase after that decade, especially in the Northwest, Subandes, Transition, and East regions. In autumn, a progressive decline until the late 1990s is detected, followed by a rapid increase in the northern and central regions, while variations in the Patagonian regions are more moderate. In winter, a rapid decrease until the mid-1970s dominates, followed by a sustained increase since 1990, significant in the Subandes, East, and Extra-Andean Patagonia regions. In spring, variations are less pronounced, pointing to a mainly linear behaviour. Annual and seasonal AI series exhibit significant climatic shifts according to the Hubert segmentation. Table 4 summarizes the shifts identified in the AI series and its associated climatic variables, their main characteristics, the year of occurrence, and the affected regions. For the annual AI, a negative shift is observed in 1997/1998 in the Northwest, Subandes, Transition, and Extra-Andean Patagonia regions (10–14% decrease), and a later negative shift in 2005/2006 in Andean Patagonia. In summer, a positive shift occurs in 1985/1987 in the Northwest and Subandes regions (70–83% increase), followed by another positive shift in 1998/2000 in the Transition and East regions (25–38% increase). In autumn, a negative shift is recorded in 1972/1973 in the Northwest and Subandes regions (26–33% decrease), while in 1980/1981 a positive shift occurs in Andean Patagonia (57% increase). Finally, in spring, a single negative shift is detected in 1974/1975 in the Northwest region, with an average decrease of 29%. No significant shifts are identified in winter using the applied method. Table 4 Characterization of climatic shifts identified in the annual and seasonal aridity index (AI), precipitation (PRE) and potential evapotranspiration (PET) series Series Variables Type of shift Timing of shift Regions of occurrence Approximate change (Δ%) Annual AI Negative 1997/1998 Northwest, Subandes, Transition, Extra-Andean Patagonia 10–14% Negative 2005/2006 Andean Patagonia 14% PRE Negative 1997/1998 Northwest, Transition 8–12% Negative 2001/2006 Subandes, Andean Patagonia, Extra-Andean Patagonia 10–12% PET Positive 1976/1977 East, Andean Patagonia, Extra-Andean Patagonia 1–2% Positive 1985/1986 Northwest 3% Positive 2002/2011 Subandes, Transition, Este, Andean Patagonia, Extra-Andean Patagonia 2–3% Summer AI Positive 1985/1987 Northwest, Subandes 70–83% Positive 1998/2000 Transition, East 25–38% PRE Positive 1985/1986 Northwest, Subandes, Transition 20–49% Positive 1996/1997 Northwest, Subandes, Transition, East 21–32% PET Positive 1977/1978 Andean Patagonia, Extra-Andean Patagonia 3–5% Positive 2006/2012 Northwest, Subandes, Transition, East 3–5% Autumn AI Negative 1972/1973 Northwest, Subandes 26–33% Positive 1980/1981 Andean Patagonia 57% PRE Negative 1979/1980 Northwest, Subandes, Transition 24–40% Positive 1980/1981 Andean Patagonia 47% PET Positive 2008/2009 Andean Patagonia 4% Winter PRE Negative 1982/1983 Subandes 36% Spring AI Negative 1974/1975 Northwest 29% PET Positive 1993/1994 East 5% PET Positive 2001/2002 Northwest, Subandes, Transition 5–6% 3.4. Temporal Variability of PRE and PET Since the AI depends on the combined behaviour of precipitation (PRE) and potential evapotranspiration (PET), their temporal variability over the 1961–2020 period was analysed for each region of Argentina. Figure 5 shows the annual and seasonal PRE and PET series spatially averaged for the six regions, together with the linear regression, the polynomial curve, and the identified climatic shifts. At both annual and seasonal scales, both variables exhibit marked interannual variability, although common temporal patterns can be identified across regions. In addition, Tables 2 and 3 present, respectively, the linear trends (B) and the adjusted coefficients of determination (adj-R²) of the annual and seasonal PRE and PET series for each region of the country, allowing an assessment of the consistency between the trends of these variables and those of the AI. Likewise, Table 4 summarizes the shifts identified in the PRE and PET series, together with their main characteristics, facilitating the evaluation of whether the discontinuities in the AI were associated with variations in one or both variables. At the annual scale (Fig. 5 a), precipitation shows a negative linear trend in nearly all regions, though only significant in Andean Patagonia (-2.48% per decade), while a slight positive trend is observed in the East region. In most regions, and in agreement with the AI, PRE displays a rapid increase until the early 1980s followed by a sustained decline to the present. In addition, negative shifts are detected in 1997/1998 in the Northwest and Transition regions and in 2005/2006 in Andean Patagonia, all coincident with negative shifts in the annual AI. Shifts between 2001 and 2006 are also identified in the Subandes and Extra-Andean Patagonia regions, which are not reflected in the annual AI. Regarding annual PET, a significant increase is observed in all regions, with changes less pronounced than those of PRE, except in the East region, where the PET increase (0.73%) exceeds the slight PRE decrease (0.02%). In the Subandes and Transition regions, PET shows a decrease until the late 1970s and early 1980s, followed by a sustained increase, whereas in the other regions the polynomial fit is very similar to the linear regression. Finally, PET records positive shifts at different times, none of which coincide with those observed in the AI for the corresponding regions. In summer (Fig. 5 b), changes in PRE more closely track variations in the AI. PRE increases in most regions, consistent with the positive AI trend, except in Andean Patagonia, where precipitation declines together with a reduction in the index. In the Transition and East, PRE decreases until the 1970s, followed by an increase until around 2010 and a subsequent decline, whereas in the Northwest, Subandes, and Andean Patagonia the polynomial fits closely resemble the linear trends. In Extra-Andean Patagonia, PRE increases until the 2000s and then decreases rapidly. PRE exhibits positive shifts in 1985/1986 and 1995/1997 in the Northwest, Subandes, Transition, and East regions, possibly associated with the positive AI shifts observed in the same periods for the Transition and East regions. In contrast, PET exhibits a positive trend and poorly defined polynomial fits in all regions, except in the Subandes and Transition regions, where it decreases slightly until the late 1980s and early 1990s and then increases toward the present. PET also shows two positive shifts (one in 1977/1978 in Andean and Extra-Andean Patagonia and another in 2006/2012 in the Northwest, Subandes, Transition, and East regions) that do not coincide with AI shifts in this season. In autumn (Fig. 5 c), PRE trends follow changes in the AI in the Northwest, Subandes, and Patagonian regions (Andean and Extra-Andean), with decreases in the first two and increases in the latter two. In the Transition and East regions, PET increases while PRE decreases; therefore, the signs of the trends do not match, and the increase in the AI cannot be attributed to precipitation alone. Regarding the non-linear behaviour of PRE, the Northwest, Subandes, Transition, and East regions show a rapid decrease until the late 1980s, followed by a stable period until the early 2000s and a slight rebound thereafter, whereas Andean and Extra-Andean Patagonia exhibit a decrease during the 1960s followed by a rapid increase until the late 2000s and a subsequent decline to the present. PRE shows a positive shift in 1980/1981 in Andean Patagonia, coincident with an autumn AI shift in the same period. PET does not exhibit clear trends in most regions, except in Andean and Extra-Andean Patagonia, where a positive linear trend is observed together with a slight decrease until the late 1990s, followed by a rapid increase toward the present. PET also presents a positive shift in 2008/2009 in Andean Patagonia that is not reflected in the AI. In winter (Fig. 5 d), most regions show a decrease in PRE consistent with the negative AI trends. In Andean Patagonia and the Northwest, positive trends in both PRE and PET are observed, though with different effects: in Andean Patagonia, the positive PET trend exceeds that of PRE and leads to a decrease in the AI, whereas in the Northwest both trends are non-significant and fail to explain the AI change. Polynomial trends in PRE differ among regions: the Northwest and Andean Patagonia show a decrease until the late 1970s followed by an increase until the mid-2000s and a subsequent decline; the Subandes and Extra-Andean Patagonia regions show a rapid decrease until the early 1990s and a slight increase thereafter; and the Transition and East regions show a polynomial fit similar to the linear one. PET generally exhibits weak linear trends and polynomial fits that are like the linear trend, except in the Northwest, where a slight decrease is observed since the early 2000s. No significant winter shifts are detected in PRE or PET in most regions, consistent with the absence of AI shifts, with the sole exception of a negative shift in the Subandes region in 1982/1983 that is not reflected in the index. In spring (Fig. 5 e), PRE trends again follow AI trends in most regions, with negative values in the Northwest and Patagonia (Andean and Extra-Andean) and positive values in the East and Transition regions. In the Subandes, however, a negative PRE trend and a positive PET trend are observed, which do not align with the non-significant positive AI trend and indicate a decoupling between the climatic variables and the index. In terms of non-linear behaviour, PRE shows marked regional differences: the Northwest, Subandes, Transition, and East regions experience a rapid decrease until the mid-1970s, followed by an increase until the mid-2000s and a new decline toward the present; Andean Patagonia shows an increase until the early 1980s followed by a sustained decrease; and Extra-Andean Patagonia shows a polynomial fit similar to the linear trend. PET maintains a positive trend in most regions. In Subandes, Transition, and East, a decrease is observed until the late 1970s, followed by a progressive increase toward the present, whereas in the Northwest and in Patagonia (Andean and Extra-Andean) the polynomial fit is very similar to the linear trend. In addition, PRE shows no shifts in any region in spring, while PET exhibits two positive shifts in 1993/1994 and 2001/2002 that do not coincide with the AI shift in 1974/1975 in the Northwest, indicating that the latter cannot be attributed to either PRE or PET. The relationship between the annual and seasonal linear PRE and PET trends and the AI trends (Table 2 ) shows differences across the regions of the country. In the Northwest, the annual mean and the transitional seasons (autumn and spring) show that the negative AI trend results from decreasing PRE combined with increasing PET, whereas in summer the simultaneous increase of both variables leads to an increase in the AI, and in winter the non-significant trends in PRE and PET (3.31% and 0.15%) do not explain the observed AI change. In the Subandes region, annual, summer, and autumn trends replicate the Northwest pattern, while in winter decreasing precipitation reduces the index and in spring a nearly neutral AI is linked to slightly negative PRE offset by positive PET. In the Transition region, the annual mean and summer, winter, and spring seasons show changes like those of the previous regions, with AI and PRE increases in summer and decreases in the annual mean, winter, and spring, while in autumn a slight AI increase may be related to marginally negative PRE compensated by positive PET. In the East, the nearly neutral annual AI trend is associated with trendless PRE and markedly positive PET, while in summer and spring AI exhibits a positive trend associated with positive trends in PRE and PET; in winter, AI decreases mainly due to the negative trend in PRE, and in autumn its positive trend is not directly explained by either a decrease in PRE or an increase in PET. In Andean Patagonia, annual, summer, and spring AI decrease due to negative PRE trends, autumn AI increases in response to a positive PRE trend, and winter AI decreases because PET shows a more pronounced positive trend than PRE. In Extra-Andean Patagonia, annual and seasonal AI trends are primarily controlled by precipitation, with decreases in the annual mean, winter, and spring, and increases in summer and autumn. The adjusted coefficients of determination (adj-R 2 ) of the polynomial models for the annual and seasonal PRE and PET (Table 3 ) series are associated with the adj-R 2 values of the AI in different ways across the regions of the country. In the Northwest, AI shows the best polynomial fit in summer, mainly modulated by PRE, whereas in the annual mean and the other seasons AI structures are weaker due to the low explanatory power of PRE and PET, especially in winter. In the Subandes region, AI exhibits a robust fit in summer supported by a strong non-linear structure in PRE and a smaller contribution from PET; in autumn, the low adj-R 2 of AI (0.08) does not reflect the coherent structure of PRE with a high adj-R 2 (0.36); and in the annual mean, winter, and spring, fits are weak and only partially explained by PRE or PET. In the Transition region, polynomial models of AI again stand out in summer with PRE as the dominant signal, whereas in the annual mean and other seasons the fits are smaller or weak for both AI and PRE and PET. In the East region, AI shows weak fits at both annual and seasonal scales, while a well-defined fit is observed in annual PET (adj-R 2 = 0.31) and in summer PRE (adj-R 2 = 0.17), indicating that the non-linear behaviour of the variables is not reflected in the AI changes. In Andean Patagonia, AI shows consistent non-linear modelling at annual and autumn scales in agreement with PRE and reinforced by PET, whereas all three variables exhibit poor fits in summer, winter, and spring. In Extra-Andean Patagonia, AI maintains low fits, with only autumn showing a significant model aligned with PRE, while PET exhibits a more pronounced structure at annual and summer scales that is not reflected in the AI. 3.5. Climatological Changes in the AI and Contribution of PRE and PET The changes in the annual and seasonal AI between 1961–1990 and 1991–2020 were analysed, and the contributions of precipitation and PET to these changes were quantified (Fig. 6 ). This approach allows identification, from a climatological perspective, of the specific role of each variable in the AI variations at both annual and seasonal scales. The comparison between these two 30-year periods provides a representation of the mean change, capturing long-term trends and structural differences, which facilitates the description of an average climate change with adequate statistical stability. The annual AI (Fig. 6 a) shows a decrease over most of the country, more pronounced and significant in the Principal and Patagonian Andes between 34° and 45°S, while some areas—such as southwestern Buenos Aires (around 40°S and 64°W), southern Mesopotamia, and southern Patagonia—exhibit non-significant increases. These patterns vary by season: in summer (Fig. 6 b), increases dominate in the centre and north (significant in the central-northwestern region), contrasting with significant decreases in Patagonia and the Andes; in autumn (Fig. 6 c), the pattern reverses, with strong decreases in the centre and north and significant increases in Patagonia and the Andes south of 30°S; during winter (Fig. 6 d), negative changes prevail (more intense in Patagonia), although areas of index increase appear east of the Sub-Andean Ranges, the Puna, and the Frontal Andes; in spring (Fig. 6 e), non-significant positive changes are observed over the Chaco-Pampean Plains (east of 67°W), while significant decreases dominate in the Andes Mountain Range and Patagonia (between 30° and 45°S). The contributions of annual and seasonal precipitation (Fig. 6 f-j) display spatial patterns very similar to those of the AI, though with slightly more extensive significant areas. For example, in summer, significant positive contributions extend from the northwest and central regions to the Atlantic coast. Likewise, in spring, the significant decrease over the Andes reaches parts of the Patagonian Plateau. This suggests that precipitation modulates the spatial structures of AI change, although in some regions these patterns may be strengthened or weakened by the effect of PET. Overall, PET shows more moderate contributions than precipitation. At the annual scale (Fig. 6 k), significant increases are recorded over much of Argentina, more pronounced in the Andes between 33° and 50°S. At the seasonal scale, positive changes dominate, with differences among seasons: in summer (Fig. 6 l), increases are significant only in a belt north of the Patagonian Plateau; in autumn and winter (Fig. 6 m-n), most of the territory shows no significant changes, although in winter very strong increases occur along the Andes Mountain Range, exceeding 30% in the Frontal, Principal, and Patagonian Andes; in spring (Fig. 6 o), central and northern Argentina show positive values, significant in Mesopotamia, the Chaco Plains, and the Sub-Andean Ranges up to the Puna. This suggests that evaporative demand plays a secondary role in the AI changes; however, by contributing positively at both annual and seasonal scales, it can intensify AI change patterns (as in the Patagonian Andes during summer) or attenuate them (as in northeastern Argentina during spring). For a more detailed analysis, changes in the AI and the contributions of PRE and PET were calculated at annual and seasonal scales, spatially averaged for each region of the country, by comparing the mean values of the 1961–1990 and 1991–2020 periods. At the annual scale (Fig. 7 a), a negative change in the AI is observed in all regions, with the largest decrease in Andean Patagonia (7.9%). PET shows significant increases in all cases (0.4–2.3%), while precipitation exhibits non-significant decreases in most regions (1.1–6.3%), except for a slight increase in the East (0.7%). In this latter region, the change in the AI is driven by the more pronounced increase in PET (2.3%), which exceeds the contribution of precipitation. During summer (Fig. 7 b), changes in the AI are mainly controlled by precipitation, as PET shows small and non-significant increases in most regions. In the Northwest, Subandes, Transition, and East regions, increases in the index are associated with significant increases in precipitation (10.9–39.3%), while in Extra-Andean Patagonia this increase is much smaller (0.4%). In Andean Patagonia, the index decreases by 7.9% due to a reduction in precipitation combined with a significant increase in PET (0.5%). In autumn (Fig. 7 c), the Northwest, Subandes, and Transition regions exhibit decreases in the index linked to a significant reduction in precipitation (11.5–26.5%), while in the East this decrease is more moderate (3.7%). In contrast, Andean and Extra-Andean Patagonia show significant increases in the AI driven by increased precipitation, with Andean Patagonia standing out due to the strong contribution of this variable (37.6%). PET plays a secondary role in this season, with positive contributions ranging from 0.6 to 3.5%. In winter (Fig. 7 d), most regions experience a reduction in the AI, which is significant in the Subandes and Transition regions. Andean Patagonia represents a particular case, as PET contributes more than precipitation (49.5% versus 30.2%), resulting in a marked decrease in the index (19.3%). By contrast, the Northwest shows an increase in the AI (8.5%) associated with an increase in precipitation (16.3%) that is not fully offset by the positive contribution of PET (7.8%). Finally, spring (Fig. 7 e) is characterized by index changes once again dominated by precipitation, with a mixed pattern of increases (Subandes, Transition, and East) and decreases (Northwest, Andean and Extra-Andean Patagonia), while PET maintains a secondary role with positive contributions between 0 and 4.8%. Andean Patagonia again shows the largest decrease in the index (22.3%), explained by a pronounced decline in precipitation (21.2%) together with a slight increase in PET (1.1%). 3.6. Climatic Shifts in the AI and Contribution of PRE and PET Changes in the AI and the contributions of precipitation and PET were also analysed by comparing periods defined by the climatic shifts of the AI itself (Fig. 8 and Table 4 ). Unlike analyses based on fixed climatic periods, where long-term averages may mask abrupt transitions, the use of variable periods allows identification of why the AI changes at specific times, since the temporal boundaries emerge from its own dynamics. This approach reveals how precipitation and PET respond during intervals when the index undergoes significant variations, facilitating the identification of their dynamic changes and the processes that accompany them. At the annual AI shifts, opposite and significant contributions are identified: negative precipitation and positive PET strongly reduce the AI. The negative shift of 1997/1998, detected in the Northwest, Subandes, Transition, and Extra-Andean Patagonia regions, shows an extensive area of significant decrease from the northwest to Patagonia, associated with the combination of negative precipitation and positive PET, both significant (Figs. 8 a, 8 h, 8 o). The 2005/2006 shift in the Andean Patagonia region exhibits a significant decrease in the index and precipitation in the Principal and Patagonian Andes, while PET in the same region shows a slight but significant increase (Figs. 8 b, 8 i, 8 p). For summer AI shifts, the significant increase in the index is mainly related to a positive and significant precipitation contribution, as PET shows only slight positive contributions across most of the country. In the positive shift of 1975/1987, recorded in the Northwest and Subandes regions, the index increase coincides with significant positive contributions from both variables, though more pronounced for precipitation (Figs. 8 c, 8 j, 8 q). The positive shift of 1998/2000 in the Transition and Eastern regions shows the same pattern, with a significant increase in precipitation and a slight, non-significant increase in PET. In this sense, although PET shows significant changes only in Patagonia during summer, it neither determines nor contributes significantly to summer AI shifts (Figs. 8 d, 8 k, 8 r). For autumn AI shifts, the pattern observed in summer is repeated: precipitation controls the index change, while PET contributes very little. The negative shift of 1972/1973 in the Northwest and Subandes regions shows a broad area of significant index decrease over central and northern Argentina, consistent with a significant decline in precipitation and a slight, non-significant decrease in PET in those regions (Figs. 8 e, 8 l, 8 s). The positive shift of 1981/1982 in Andean Patagonia shows a significant index increase explained by the positive contribution of precipitation, while PET displays slightly positive contributions in the northern part of the region (30°-40°S) and negative contributions in the south (40°-50°S) (Figs. 8 f, 8 m, 8 t). Together, these results confirm that, as in summer, autumn index changes depend on precipitation, since evaporative demand shows only weak and non-significant contributions. For the spring AI shift, changes are more moderate than in summer and autumn. In the Northwest, where the negative shift of 1974/1975 was recorded, the most pronounced and widespread index decrease across much of the country is identified, associated with a significant negative contribution from precipitation, especially in the Frontal and Principal Andes, while PET shows a slight, non-significant increase (Figs. 8 g, 8 n, 8 u). This pattern is like that of the annual AI shifts, where the combination of negative precipitation and positive PET determines the index decrease. The contributions of PRE and PET to the total change in AI were quantified at annual and seasonal scales, spatially averaged for each region of the country, by comparing the mean values of the periods defined by the climatic shifts of the index itself (Fig. 9 and Table 4 ). When comparing the periods defined by the climatic shifts detected in the annual AI series (1997/1998 and 2005/2006), a decrease in the AI is observed in all regions (Figs. 9 a and 9 b). This reduction is statistically significant in most cases and is mainly explained by negative contributions from precipitation accompanied by small positive contributions from PET. Andean Patagonia stands out as the region with the largest decreases in both shifts, with significant changes of 10.6% in the first and 17% in the second. In the East, a reduction in the AI is also observed, associated with a non-significant decrease in precipitation and a significant increase in PET, whose contribution in the 2005/2006 shift (3.4%) exceeds that of precipitation (0.9%), resulting in a negative index change of 2.5%. For the shifts identified in the summer series (1985/1987 and 1998/2000), significant increases in the AI are observed in the Northwest, Subandes, Transition, and East regions (Figs. 9 c and 9 d), related to marked increases in precipitation and a secondary role of PET. The Subandes concentrate the largest magnitudes of change in both shifts (41.7% in 1985/1987 and 36.9% in 1998/2000), with significant precipitation increases between 11% and 18%. In contrast, Andean and Extra-Andean Patagonia show minimal changes in both cases, well below those recorded in the rest of the country. The shifts in the autumn series (1972/1973 and 1980/1981) show an AI decrease in the Northwest, Subandes, Transition, and East regions, explained by strong negative contributions from precipitation (Figs. 9 e and 9 f). The opposite occurs in Andean and Extra-Andean Patagonia, where the index increases due to positive precipitation contributions, more pronounced in Andean Patagonia (28% in 1972/1973 and 42% in 1980/1981). PET maintains a secondary role in both shifts, with small variations compared to those of precipitation, although the East region presents the highest values (4.1% and 3% in each shift). The spring shift recorded in 1974/1975 (Fig. 9 g) is characterized by a decrease in the AI in most regions. The Northwest stands out as the only region where this reduction is significant (10.7%), associated with a decline in precipitation (9.1%) together with a slight increase in PET (1.6%). In contrast, the East shows an increase in the index because of an increase in precipitation (5.4%) that offsets the slight negative contribution of PET (0.01%). 4. DISCUSSION AND CONCLUSIONS The AI evolution in Argentina during the 1961–2020 period reflects an integrated response to changes in water availability and atmospheric evaporative demand. In this context, precipitation emerges as the primary driver of the spatial and temporal signals of the index, while PET modulates their intensity and extent. This behaviour is consistent with global studies showing that precipitation explains most of aridity variability, whereas warming has steadily increased the relative contribution of PET (Yang et al., 2019 ; Pan et al., 2021 ). Along these lines, AI linear trends are largely determined by precipitation trends, which define both the sign and the spatial structure of the changes, while increases in PET intensify aridification signals and promote the expansion of affected areas. This pattern is consistent with the global expansion of arid and semiarid regions documented in recent decades, attributed to rising PET and heterogeneous changes in precipitation (Feng and Fu, 2013 ). In turn, AI non-linear trends reveal a dynamic dominated by precipitation variability associated with low-frequency oscillations, together with a more localized and seasonal contribution of PET, which either reinforces or attenuates these signals depending on the region. Similar results were reported for China, where increases in aridity were linked to interdecadal transitions in temperature and precipitation (Li et al., 2017 ; Liu et al., 2018 ). Overall, these findings indicate that aridity responds to complex climatic dynamics that cannot be interpreted solely through linear trends. At the national scale, AI changes in Argentina are mainly controlled by precipitation, which regulates the sign, magnitude, and spatial structure of its variations, both when considering 30-year mean values and periods defined by climatic shifts. Under both approaches, precipitation stands out as the primary explanatory factor of the spatio-temporal pattern of aridity, indicating that transitions in the aridity regime are associated with persistent or abrupt changes in water availability. This behaviour is consistent with studies conducted in different regions of Argentina, which documented a transient reduction in aridity in the central part of the country during the twentieth century, linked to increased precipitation and accompanied by greater seasonality and spatial concentration (Rusticucci and Penalba, 2000 ; Minetti et al., 2003 ; Penalba and Vargas, 2004 ; De la Casa and Ovando, 2014 ; Barros et al., 2015 ; Doyle, 2020 ). In contrast, PET shows more moderate and spatially limited contributions, with a predominantly positive signal that modulates the intensity of index changes by amplifying decreases when it coincides with reductions in precipitation or attenuating them when both variables act in opposite directions. This result is comparable with previous studies indicating that warming and the intensification of thermal extremes have increased atmospheric water demand, particularly in the Pampas and Patagonia regions (Ferrelli et al., 2021 ; Müller et al., 2021 ; Brendel et al., 2025 ; Barros et al., 2015 ), such that even in areas with higher precipitation, aridity may intensify due to increasing PET and the recurrence of dry periods (Llano and Penalba, 2011 ; Rivera et al., 2013 ; Panza et al., 2025 ). Therefore, the analysis based on fixed 30-year periods allowed the characterization of the mean change in the index under a stable climatological framework, whereas periods defined by climatic shifts revealed the internal dynamics of these changes, showing that the most intense aridification episodes occur when decreases in precipitation are combined with increases in evaporative demand. Overall, the consistency between both approaches reinforces the need to interpret aridity from an integrated water-balance perspective, in which precipitation constitutes the main water input and PET acts as a key modulating factor of changes in the index. In Argentina, long-term changes in aridity result from the combination of linear trends, nonlinear dynamics, and climatic shifts. Although the AI shows a generalized process of aridification at the national scale, in line with what was reported by Blanco and Doyle ( 2024 ) and with global studies documenting an aridity intensification in both drylands and humid regions (Daramola and Xu, 2022 ; Luo et al., 2023 ), the regional and seasonal analysis reveals contrasts in the sign, intensity, timing, and climatic factors driving these changes. Thus, the northern and central regions of the country exhibit more complex and variable temporal patterns, with more pronounced changes in summer and autumn, whereas Patagonia shows more gradual variations, dominated by linear trends and low-frequency oscillations. In most regions and seasons, AI changes are mainly controlled by precipitation, which governs both trends and abrupt shifts; however, in central and eastern sectors of the country, particularly in autumn and winter, PET plays a more relevant modulating role, amplifying or attenuating the index signals depending on its interaction with precipitation. This behaviour is consistent with previous evidence indicating that increases in PET contribute to the intensification of global droughts, while their distribution and variability are modulated by precipitation (Wang et al., 2022 ). Overall, these results show that national averages tend to obscure key processes, especially in countries with pronounced topographic and climatic diversity such as Argentina (Beck et al., 2018 ; Pereyra et al., 2019) and reinforce the need to address aridity through an integrated regional analysis of the water balance. The contributions of precipitation and PET to the AI change in the different regions of the country were evaluated, comparing both the averages of fixed 30-year periods and those defined by climatic shifts. In the Northwest, Subandes, and Transition regions, AI variations are dominated by precipitation, indicating that aridity responds mainly to changes in water supply, while evaporative demand plays a secondary role. In the East region, by contrast, PET determines the annual aridification signal and reinforces decreases in the index during abrupt changes, acting as a key forcing even when precipitation variations are weak or not significant. This pattern agrees with the observations of Ruscica et al. ( 2022 ), who associated the increase in evapotranspiration in southeastern South America with local changes in land cover, highlighting the importance of evaporative demand as a modulator of regional aridity. In Extra-Andean Patagonia, AI changes are small, with moderate contributions from precipitation and a marginal role of PET, whereas in Andean Patagonia the decrease in precipitation and the significant increase in PET lead to increases in aridity, reflecting an efficient coupling between water and energy forcings. These findings are consistent with studies documenting the intensification of negative precipitation trends in the Patagonian Andes (Martin et al., 2022 ; Oruezabal et al., 2023 ) and the recent transition toward warmer and drier conditions, with impacts on glaciers and river flows (Rivera et al., 2021 ; Hurtado et al., 2023 ; Ricetti et al., 2025 ). Overall, the results show that precipitation governs the direction and spatial structure of aridity changes in Argentina, while PET acts as a regional amplifying factor capable of intensifying aridification during climate transitions. This study provides detailed evidence on the climatic factors controlling aridification in Argentina by offering an explicit and regionally differentiated quantification of the contributions of precipitation and PET to changes in aridity, an aspect that has been little explored in the literature, especially in the Southern Hemisphere (Ma and Fu, 2007 ; Zarch et al., 2015 ; Zhang et al., 2023 ). By integrating linear trends, nonlinear dynamics, and climatic shifts within a single analysis, the traditional approach based solely on long-term averages is surpassed, demonstrating that aridification can intensify abruptly when changes in water supply and atmospheric demand converge. This has direct implications for assessing climate vulnerability, as it allows the identification of regions where aridification is dominated by precipitation deficits and others where increasing evaporative demand is a critical factor, which is essential for guiding adaptation and mitigation strategies. Identifying the climatic factors that control aridity provides a solid scientific basis for the sustainable management of water resources in areas vulnerable to desertification, where small changes in the water balance can cause disproportionate impacts on ecosystems and human activities (Berdugo et al., 2020 ; Zhang et al., 2021 ; Chen et al., 2022 ). In the context of global climate change, our results offer a more accurate view of aridification, which can help improve environmental and living conditions for populations affected by increasing water scarcity, as in Andean Patagonia (Brendel et al., 2022 ), and mitigate the effects of adverse processes such as soil degradation and desertification in central Argentina (Torres et al., 2015 ; Civeira and Rodriguez, 2023 ). Declarations Author Contribution statement Pedro Samuel Blanco carried out the research, produced the figures, and wrote the manuscript. Moira Evelina Doyle performed a critical review to ensure the intellectual rigor of the content. Financial Support This work was supported by the National Scientific and Technical Research Council of Argentina (PIP KE2 11220210100752CO). Conflict of Interest statement The authors reported no potential conflicts of interest. Author Contribution Pedro Samuel Blanco carried out the research, produced the figures, and wrote the manuscript. Moira Evelina Doyle performed a critical review to ensure the intellectual rigor of the content. Acknowledgement The authors express their gratitude for the comments provided by the editors and reviewers, which have been valuable in improving the quality of the research. Data Availability The gridded dataset used in this study is publicly accessible and can be obtained from the following link: CRU TS 4.06 - [https://crudata.uea.ac.uk/cru/data/hrg/cru\_ts\_4.06/](https:/crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.06) (Harris et al. 2020). References Barros VR, Boninsegna JA, Camilloni IA, Chidiak M, Magrín GO and Rusticucci M (2015) Climate change in Argentina: trends, projections, impacts and adaptation. 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Science 382 , 579-584. https://doi.org/10.1126/science.adh0716 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Apr, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers agreed at journal 09 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 05 Feb, 2026 Submission checks completed at journal 05 Feb, 2026 First submitted to journal 03 Feb, 2026 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-8781542","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589099254,"identity":"d759c41c-a6e7-439f-b47b-e1bc3678d8d0","order_by":0,"name":"Pedro Samuel Blanco","email":"data:image/png;base64,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","orcid":"","institution":"Universidad de Buenos Aires","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"Samuel","lastName":"Blanco","suffix":""},{"id":589099255,"identity":"d3cc520c-1914-49c0-81e1-ac76d21f49a0","order_by":1,"name":"Moira Evelina Doyle","email":"","orcid":"","institution":"Universidad de Buenos Aires","correspondingAuthor":false,"prefix":"","firstName":"Moira","middleName":"Evelina","lastName":"Doyle","suffix":""}],"badges":[],"createdAt":"2026-02-04 04:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8781542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8781542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102596047,"identity":"a6b540b1-41ac-46e6-aaa3-41f067f91673","added_by":"auto","created_at":"2026-02-13 12:14:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":381265,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Geographic location and topography of the continental sector of Argentina; \u003cstrong\u003e(b)\u003c/strong\u003e annual average field of the aridity index (AI) for the 1961-2020 period and spatial distribution of the characteristic AI isolines; and \u003cstrong\u003e(c)\u003c/strong\u003e regions of Argentina delineated based on AI and topography\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/388ac213e1c1371aa2f2437f.png"},{"id":102747090,"identity":"37865ff0-8284-4316-8431-82cccd136e1e","added_by":"auto","created_at":"2026-02-16 09:03:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":679198,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the annual and seasonal linear trend of \u003cstrong\u003e(a-e)\u003c/strong\u003e the aridity index, \u003cstrong\u003e(f-j)\u003c/strong\u003e precipitation, and \u003cstrong\u003e(k-o)\u003c/strong\u003e potential evapotranspiration. Values are expressed as the percentage of the trend relative to the average of the study period. The dotted areas indicate a statistically significant linear trend at the 95% confidence level, assessed using the Student’s t-test\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/00ec89d49e77229e919e6d70.png"},{"id":102747743,"identity":"b29981d5-34fd-42df-81d9-2389a445e4be","added_by":"auto","created_at":"2026-02-16 09:05:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":697219,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the adjusted coefficient of determination (adj-R\u003csup\u003e2\u003c/sup\u003e) of the third-order polynomial trend model for \u003cstrong\u003e(a-e)\u003c/strong\u003e the aridity index, \u003cstrong\u003e(f-j)\u003c/strong\u003e precipitation, and \u003cstrong\u003e(k-o)\u003c/strong\u003e potential evapotranspiration. The dotted areas indicate a statistically significant third-order polynomial trend at the 95% confidence level, assessed using the Fisher test\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/4ba537d07ff48b4e1edf2b2c.png"},{"id":102596051,"identity":"dae3207d-1eb0-4470-8d6e-2973e6f55014","added_by":"auto","created_at":"2026-02-13 12:14:27","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":450344,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and seasonal time series of the aridity index (AI) for the different regions of Argentina during the 1961-2020 period. The columns present the regional AI series corresponding to \u003cstrong\u003e(a)\u003c/strong\u003e annual, \u003cstrong\u003e(b)\u003c/strong\u003e summer, \u003cstrong\u003e(c)\u003c/strong\u003e autumn, \u003cstrong\u003e(d)\u003c/strong\u003e winter, and \u003cstrong\u003e(e)\u003c/strong\u003e spring. Linear trends, third-order polynomial trends, and Hubert segmentation are shown\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/724da5f75b84d9e515826de5.png"},{"id":102596052,"identity":"0de725ea-1d5f-4da4-8816-d0c1d9dee274","added_by":"auto","created_at":"2026-02-13 12:14:27","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":458994,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and seasonal time series of the precipitation (PRE) and potential evapotranspiration (PET) for the different regions of Argentina during the 1961-2020 period. The columns present the regional PRE and PET series corresponding to \u003cstrong\u003e(a)\u003c/strong\u003e annual, \u003cstrong\u003e(b)\u003c/strong\u003e summer, \u003cstrong\u003e(c)\u003c/strong\u003e autumn, \u003cstrong\u003e(d)\u003c/strong\u003e winter, and \u003cstrong\u003e(e)\u003c/strong\u003e spring. Linear trends, third-order polynomial trends, and Hubert segmentation are shown\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/8ea40dc39f8b96dc42168192.png"},{"id":102596053,"identity":"2fb171f3-3a80-4e87-9233-e9f807f665b7","added_by":"auto","created_at":"2026-02-13 12:14:28","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":429930,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and seasonal changes of \u003cstrong\u003e(a-e)\u003c/strong\u003e the aridity index and contributions of \u003cstrong\u003e(f-j)\u003c/strong\u003e precipitation and \u003cstrong\u003e(k-o)\u003c/strong\u003e potential evapotranspiration between the periods 1961-1990 and 1991-2020 are compared. The dotted areas indicate a statistically significant contribution at the 95% confidence level, assessed using the Student’s t-test\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/c50f82efba4d3266015ab3f9.png"},{"id":102747096,"identity":"7c475573-99c4-4305-a742-372df7e1da37","added_by":"auto","created_at":"2026-02-16 09:03:49","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":92861,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual and seasonal changes of the aridity index (AI) and contributions of precipitation (PRE) and potential evapotranspiration (PET) between the periods 1961-1990 and 1991-2020, spatially averaged for the different regions of Argentina. The regional changes in the AI, PRE, and PET are shown for \u003cstrong\u003e(a)\u003c/strong\u003e annual, \u003cstrong\u003e(b)\u003c/strong\u003e summer, \u003cstrong\u003e(c)\u003c/strong\u003e autumn, \u003cstrong\u003e(d)\u003c/strong\u003e winter, and \u003cstrong\u003e(e)\u003c/strong\u003e spring. Statistically significant contributions at the 95% confidence level, assessed using the Student’s t-test, are indicated with an asterisk on the left side of the figure. Error bars represent the standard deviation (±1σ) of all grid points within each region of the country\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/7d4f41c845ef8af6ab3fd2af.png"},{"id":102596050,"identity":"b0e98e20-6444-4b4b-81e0-8f9344d851de","added_by":"auto","created_at":"2026-02-13 12:14:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":401650,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of \u003cstrong\u003e(a-g)\u003c/strong\u003e the aridity index and contributions of \u003cstrong\u003e(h-m)\u003c/strong\u003e precipitation and \u003cstrong\u003e(o-u)\u003c/strong\u003e potential evapotranspiration between the periods defined by climatic shifts. The dotted areas indicate a statistically significant contribution at the 95% confidence level, assessed using the Student’s t-test\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/c7fd7e4867179ba0c4f5e166.png"},{"id":102747262,"identity":"35515a68-0abb-49b3-9133-1d851e201ddc","added_by":"auto","created_at":"2026-02-16 09:04:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":148137,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the aridity index (AI) and contributions of precipitation (PRE) and potential evapotranspiration (PET) between the periods defined by climatic shifts, spatially averaged for the different regions of Argentina. The regional changes in the AI, PRE, and PET are shown for \u003cstrong\u003e(a)\u003c/strong\u003e annual, \u003cstrong\u003e(b)\u003c/strong\u003e summer, \u003cstrong\u003e(c)\u003c/strong\u003e autumn, \u003cstrong\u003e(d)\u003c/strong\u003e winter, and \u003cstrong\u003e(e)\u003c/strong\u003e spring. Statistically significant contributions at the 95% confidence level, assessed using the Student’s t-test, are indicated with an asterisk on the left side of the figure. Error bars represent the standard deviation (±1σ) of all grid points within each region of the country\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/b391889b77269b99d5827a8e.png"},{"id":102750855,"identity":"de7db689-b8b0-435d-8d6c-31377fe9cacb","added_by":"auto","created_at":"2026-02-16 09:22:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4767216,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8781542/v1/37511ca2-177a-4a2d-a62d-4815575394d1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eContribution of Precipitation and Potential Evapotranspiration to Long-Term Changes in Aridity in Argentina Over Recent Decades\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eIn recent decades, climate change has significantly altered the global hydrological cycle, manifesting in changes in precipitation regimes, a sustained increase in evaporation and evapotranspiration, a higher occurrence of extreme hydrometeorological events such as droughts and floods, and a reduction in mean streamflow across extensive arid and semiarid regions (Kundzewicz, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tang, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rivera et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang and Liu, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As a result, a persistent increase in aridity has been documented in various regions worldwide, including northern Russia, the Gobi and Taklamakan deserts, eastern China, the Arabian Peninsula, western and eastern Africa, northwestern Canada, Patagonia, among others (Prăvălie et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This process, known as aridification, represents a growing threat to ecosystems and human societies, as it intensifies the frequency and severity of droughts, reduces surface and groundwater availability, and negatively affects biological productivity and ecosystem services (Greve et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gr\u0026uuml;nig et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Berdugo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this context, the study of aridity across different spatial and temporal scales is crucial to understanding its dynamics, assessing its impacts, and providing a scientific basis for the sustainable management of water and land resources.\u003c/p\u003e \u003cp\u003eA fundamental characteristic of aridity is the persistent scarcity of water which, in terms of the water balance, occurs when precipitation inputs are lower than the atmospheric evaporative demand, usually represented by evapotranspiration (Nicholson, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cherlet et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This hydroclimatic condition imposes strong constraints on ecosystems and productive activities, which must adapt to limited water availability on a seasonal or permanent basis (Huang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Koutroulis et al., 2019; Moreno-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In this sense, increasing aridity in regions with low water availability intensifies environmental and socioeconomic vulnerability, affecting nearly 40% of the world\u0026rsquo;s population and exacerbating issues such as declines in agricultural production, restrictions on access to water, and soil degradation associated with desertification processes (Moral et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pellicone et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sardans et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, identifying and quantifying changes in aridity, as well as their underlying climatic components, is essential for proper risk assessment and the design of adaptation strategies in regions highly dependent on water resources.\u003c/p\u003e \u003cp\u003eAridity is a multidimensional phenomenon resulting from the interaction of atmospheric, edaphic, hydrological, ecological, and human factors (Ullah et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Depending on these factors, different types of aridity can be distinguished (Lian et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e): atmospheric aridity (greater atmospheric water demand relative to precipitation inputs), edaphic or agricultural aridity (soil water stress), hydrological aridity (deficits in surface runoff), and ecological aridity (insufficient moisture to sustain vegetation growth). In the context of climate change, atmospheric aridity has received increasing attention, as rising air temperatures have intensified atmospheric evaporative demand and altered precipitation patterns across large regions of the world, with projections indicating a further intensification of these conditions in the future (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Koutroulis et al., 2019; Moreno-Jim\u0026eacute;nez et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This scenario has motivated numerous studies to examine not only observed changes in aridity using indices that quantify it but also trends in the climatic variables that control it, such as precipitation and temperature (Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Accordingly, many studies have adopted water-balance-based approaches that consider precipitation as the input variable and potential evapotranspiration (PET), estimated from air temperature, as the output variable. By relying on widely available data and reproducible methods, these approaches make it possible to characterize changes in aridity and to assess how precipitation and PET contribute to those changes (Feng and Fu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Carvalho et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Nevertheless, global-scale studies that jointly analyse the evolution of aridity and the relative contribution of these climatic variables remain scarce (Feng and Fu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fu and Feng, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lin et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral global studies have analysed historical and projected trends in aridity and have attributed these changes mainly to precipitation and PET. An expansion of arid and semiarid regions has been documented over recent decades, associated with increasing PET and heterogeneous changes in precipitation (Feng and Fu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Consistently, a recent intensification of aridity has been observed in global drylands, along with drying trends in humid and subhumid regions, resulting from the combined effects of precipitation and PET (Daramola and Xu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Along similar lines, increases in PET have been identified as a key contributor to the rise in global droughts, although regional variations in drought duration and intensity are strongly modulated by precipitation (Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It has also been shown that precipitation explains most of the changes in aridity across large portions of the world\u0026rsquo;s drylands, while warming has steadily increased the relative contribution of PET (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, these studies tend to emphasize aggregated patterns and do not examine how the relative contribution of both drivers varies at regional or national scales or how it evolves over time, which limits the identification of dominant mechanisms in climatically heterogeneous regions.\u003c/p\u003e \u003cp\u003eIn Argentina, some studies have reported significant changes in aridity over recent decades. At the national scale, increasing aridity has been observed across extensive regions, particularly along the western mountainous belt (Blanco and Doyle, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while opposite signals have been reported in the southwestern Pampean Plains, where a retreat of arid and semiarid areas has been mainly associated with changes in precipitation (Casa\u0026ntilde;as et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In central regions, increased precipitation during the twentieth century temporarily reduced aridity, although accompanied by greater seasonality and rainfall concentration (Rusticucci and Penalba, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Minetti et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Penalba and Vargas, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; De la Casa and Ovando, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barros et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Doyle, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In parallel, warming and the intensification of thermal extremes have increased atmospheric water demand, particularly in the Pampean and Patagonian regions (Barros et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ferrelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brendel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), so that even in areas with higher precipitation, aridity may intensify due to rising PET and the recurrence of dry periods (Llano and Penalba, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rivera et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Panza et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Regional studies further indicate a recent transition toward warmer and drier conditions in northern Patagonia and a reduction in water resources in the Andes, associated with lower precipitation and higher evaporative demand (Hurtado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rivera et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, although these studies describe patterns of aridity change and their associated climatic variables, there remains a lack of integrated analyses that examine long-term aridity evolution at the national scale and explicitly quantify the relative contribution of precipitation and PET.\u003c/p\u003e \u003cp\u003eThe objective of this study is to analyse the contribution of precipitation and PET to long-term changes in aridity in Argentina during the 1961\u0026ndash;2020 period. To this end, long-term variations\u0026mdash;including trends and significant shifts\u0026mdash;in aridity and its associated climatic variables are evaluated, and the relative contribution of precipitation and PET to total aridity change is quantified. Given Argentina\u0026rsquo;s large territorial extent and pronounced climatic diversity (Beck et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the analysis is conducted at both national and regional scales, allowing the integration of countrywide aridity evolution with a more detailed examination of regional patterns and the climatic factors that explain them.\u003c/p\u003e"},{"header":"2. DATA AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data and Aridity Index\u003c/h2\u003e \u003cp\u003eThe monthly air temperature and precipitation data for the period 1961\u0026ndash;2020 used, were obtained from version 4.06 of the Climatic Research Unit (CRU) dataset. This dataset is constructed by interpolating meteorological station observations using the angular-distance weighting method on a 0.5\u0026deg; regular grid (Harris et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and is available for all continental areas except Antarctica. For this study, data were extracted for continental Argentina, located in southern South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). To achieve greater detail in the analysis of spatial patterns of aridity and its associated variables, the data were resampled to a 0.16\u0026deg; regular grid using bilinear interpolation. This approach has been widely applied in downscaling studies and in the evaluation of global climate models, showing better performance than other interpolation methods and allowing a more detailed spatial analysis of regional climate variability (Peng et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zazulie et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rivera and Arnould, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe UNEP aridity index (1997) is defined as the ratio between precipitation (PRE [mm]) and potential evapotranspiration (PET [mm]) (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). PET was estimated using the Thornthwaite (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1948\u003c/span\u003e) method, which converts mean monthly air temperature into a \u0026ldquo;heat index\u0026rdquo; representing the energetic efficiency of temperature in generating evapotranspiration.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:AI=\\frac{PRE}{PET}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe AI was calculated at annual and seasonal scales (summer [DJF], autumn [MAM], winter [JJA], and spring [SON]). Its values can be classified into different climate types. Hyperarid (0-0.05) and arid (0.05\u0026ndash;0.2) climates are characterized by very low precipitation, insufficient to meet atmospheric demand. Semiarid (0.2\u0026ndash;0.5) and dry subhumid (0.5\u0026ndash;0.65) climates also experience water deficits, though with lower intensity. Wet subhumid (0.65-1) and humid (\u0026gt;\u0026thinsp;1) climates exhibit water availability during most or all the year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study Area and Regionalisation\u003c/h2\u003e \u003cp\u003eThe study area corresponds to the Argentina continental (2,791,810 km\u003csup\u003e2\u003c/sup\u003e), located at the southern tip of South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This territory is divided into three major topographic units that condition the regional climate (Pereyra, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e): the Andes Mountain Range (western sector), the Chaco-Pampean Plains (eastern sector), and the Patagonian Plateau (south of 40\u0026deg;S). The Andes form an extensive mountain range with marked contrasts between peaks and valleys and are subdivided into the Puna (3,000\u0026ndash;5,000 m), the Frontal Andes (4,000\u0026ndash;6,000 m), the Principal Andes (1,000\u0026ndash;4,000 m), and the Patagonian Andes (1,000\u0026ndash;3,000 m). This system also includes lower-altitude ranges such as the Sub-Andean Ranges and the Pampean Ranges, around 65\u0026deg;W. The Chaco-Pampean Plains comprises the Chaco Plains (north) and the Pampean Plains (south), with elevations below 200 m, the Argentine Mesopotamia between the Paran\u0026aacute; and Uruguay rivers, and, in its northeastern extreme, the Misiones Plateau, with elevations between 500 and 900 m. Finally, the Patagonian Plateau encompasses plateaus and basins with elevations ranging from 200 to 1,500 m.\u003c/p\u003e \u003cp\u003eConsidering Argentina\u0026rsquo;s topographic diversity, a regionalization of the country was performed using multiple criteria. First, the mean annual AI field (average for the 1961\u0026ndash;2020 period) was used, allowing the identification of homogeneous climatic structures (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Second, the topographic field was superimposed on the mean annual AI, which made it possible to distinguish regions sharing the same climatic category but differing in topographic characteristics. Finally, geographic location was evaluated to determine whether regions with the same AI category were contiguous or separated by areas with other climate types. Based on these criteria, six regions were defined (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec): \u003cem\u003eNorthwest\u003c/em\u003e (predominantly semiarid climate, encompassing the Puna and the Frontal Andes); \u003cem\u003eSubandes\u003c/em\u003e (subhumid climate, over the Sub-Andean Ranges); \u003cem\u003eTransition\u003c/em\u003e (subhumid climate, in the intermediate zone between the Chaco-Pampean Plains and the eastern Sub-Andean Ranges, the Pampean Ranges, and the northeastern Patagonian Plateau); \u003cem\u003eEast\u003c/em\u003e (humid climate, over the eastern Chaco-Pampean Plains, Argentine Mesopotamia, and the Misiones Plateau); \u003cem\u003eAndean Patagonia\u003c/em\u003e (humid climate, in the Principal and Patagonian Andes); and \u003cem\u003eExtra-Andean Patagonia\u003c/em\u003e (semiarid climate with some arid areas, mainly over the Patagonian Plateau).\u003c/p\u003e \u003cp\u003eAnnual and seasonal AI values were calculated and spatially averaged for each defined region over the 1961\u0026ndash;2020 period. A one-way analysis of variance (ANOVA) (Corobov and Overcenco, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Da Silva et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was then applied to the annual AI data, using Fisher\u0026rsquo;s F test to determine whether regional means differed significantly at the 95% confidence level. The ANOVA was also applied to seasonal AI data (not shown), yielding results like those obtained using annual AI. To identify which regions differed specifically, Tukey\u0026rsquo;s Multiple Comparisons test (Benjamini and Braun, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Corobov and Overcenco; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) was used, allowing the grouping of distributions with similar means and the detection of statistically homogeneous groups.\u003c/p\u003e \u003cp\u003eThe Fisher\u0026rsquo;s F test applied to the annual AI series indicate that at least one regional mean differs significantly from the others at the 95% confidence level (p-value\u0026thinsp;\u0026lt;\u0026thinsp;2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e). Tukey\u0026rsquo;s test (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) shows that the East and Andean Patagonia regions (Group A) have statistically similar means; the Subandes and Transition regions (Group B) form a distinct group; and the Northwest (Group C) and Extra-Andean Patagonia (Group D) regions differ statistically from each other and from the remaining groups. It is worth noting that although the Subandes and Transition regions form a single group, they differ in their geographic characteristics: the Subandes are characterized by the complex topography of the Sub-Andean Ranges, whereas the Transition region is distinguished by the change in slope between the Chaco-Pampean Plains and the mountainous areas of western Argentina. Similarly, the East and Andean Patagonia regions also differ in terms of their location and contrasting topographies (plains in the East and mountains in Andean Patagonia).\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\u003eGroups identified using Tukey\u0026rsquo;s test\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage annual AI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubandes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eD\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=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Analysis of Linear and Non-linear Trends\u003c/h2\u003e \u003cp\u003eThe linear trend was estimated from the slope of a simple linear regression. The magnitude of this slope, expressed in units of the variable per year or per decade (by multiplying by 10), quantifies the rate of change of the variable. A positive value indicates an increase in the variable, whereas a negative value reflects a decrease. To compare rates of change among variables with different units, the relative rate of change with respect to the climatic mean (B) can be used, as defined by Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:B=\\frac{\\beta\\:}{\\stackrel{-}{X}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere β is the slope of the linear regression line and X̅ is the climatic mean of the variable for the 1961\u0026ndash;2020 period. The statistical significance of the slope was assessed using Student\u0026rsquo;s t test at the 95% confidence level.\u003c/p\u003e \u003cp\u003eThe nonlinear trend was estimated using third-order polynomial regression. The significance of the parameters was evaluated with Student\u0026rsquo;s t test, and the overall significance of the model was verified using Fisher\u0026rsquo;s F test, both at the 95% confidence level. The goodness of fit was assessed through the adjusted coefficient of determination (adj-R\u003csup\u003e2\u003c/sup\u003e), which indicates the proportion of data variability explained by the model, adjusted to penalize the inclusion of irrelevant variables. An adj-R\u003csup\u003e2\u003c/sup\u003e close to 1 suggests an excellent fit, whereas low or negative values indicate that the model does not adequately explain the variability.\u003c/p\u003e \u003cp\u003eMaps of linear (B) and nonlinear (adj-R\u003csup\u003e2\u003c/sup\u003e) trends were produced for the study area and for the analysed climatic variables (AI, PRE, and PET), at both annual and seasonal scales. In addition, these trends were calculated for the spatially averaged time series corresponding to the defined regions of the country.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Detection of Climatic Shifts\u003c/h2\u003e \u003cp\u003eTo detect climatic shifts in series, the classical Hubert segmentation procedure was applied (Hubert and Carbonnel, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Hubert et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). This nonparametric technique allows the identification of one or several abrupt and significant changes in the temporal mean of a series, without requiring a reference series. The procedure consists of dividing the series into segments whose means differ significantly, based on an optimal least-squares segmentation using a step-by-step combinatorial approach (Hubert, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Jun and Bantin, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hadour et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To assess the robustness of the segmentation, Scheff\u0026eacute;\u0026rsquo;s test (Scheff\u0026eacute;, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1953\u003c/span\u003e) was employed, which determines the statistical significance of differences in means between contiguous segments (Diallo and Knudby, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Assuming stationarity of the series, this test allows identification of whether the generated segments differ significantly at the 95% confidence level.\u003c/p\u003e \u003cp\u003eHubert segmentation was applied to the annual and seasonal AI series to detect abrupt changes in the hydroclimatic conditions of the country\u0026rsquo;s regions. The same analysis was performed for PRE and PET to evaluate whether discontinuities in the AI resulted from variations in one or both variables. A positive shift indicates an increase in the AI, PRE, or PET, whereas a negative shift indicates a decrease. Each identified climatic shift was characterized by its type (positive or negative), timing, regions of occurrence, and the associated percentage change. The percentage change (Δ%) was calculated from the mean values of the variables before and after the year of the climatic shift following Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}\\%=\\frac{{X}_{t}-{X}_{t-1}}{{X}_{t-1}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere X\u003csub\u003et\u003c/sub\u003e is the mean AI, PRE, or PET after the shift and X\u003csub\u003et\u0026minus;1\u003c/sub\u003e is the mean before the shift. For example, if a shift occurred between 1980 and 1981 within the 1961\u0026ndash;2020 period, X\u003csub\u003et\u003c/sub\u003e corresponds to the mean for 1981\u0026ndash;2020 and X\u003csub\u003et\u0026minus;1\u003c/sub\u003e to the mean for 1961\u0026ndash;1980.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Estimation of the Contributions of PRE and PET to Changes in the AI\u003c/h2\u003e \u003cp\u003eChanges in the AI are attributed to changes in the climatic variables involved in its calculation. The relative contribution of PRE and PET to changes in the AI was quantified using the equation proposed by Feng and Fu (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), based on a second-order Taylor polynomial expansion (Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:\\varDelta\\:\\left(\\frac{PRE}{PET}\\right)=\\frac{1}{{PET}_{1}}\\varDelta\\:PRE-\\frac{{PRE}_{1}}{{\\left({PET}_{1}\\right)}^{2}}\\varDelta\\:PET+\\frac{{PRE}_{1}}{{\\left({PET}_{1}\\right)}^{3}}{\\left(\\varDelta\\:PET\\right)}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe left-hand side of the equation represents the change in the AI, calculated as the difference between the mean values of two defined subperiods. The first term on the right-hand side represents the relative contribution of PRE, assuming PET remains constant at the mean of the first subperiod (subscript 1), while the second and third terms indicate the relative contribution of PET, with PRE held constant. By multiplying each term by 100, these contributions can be expressed as percentages of the total change in the AI.\u003c/p\u003e \u003cp\u003eFor this study, a climatological analysis was conducted by dividing the study period into two subperiods of equal length (1961\u0026ndash;1990 and 1991\u0026ndash;2020), and changes were evaluated at both annual and seasonal scales. In this case, PRE\u003csub\u003e1\u003c/sub\u003e and PET\u003csub\u003e1\u003c/sub\u003e correspond to the mean values of the first subperiod. In addition, the same methodology was applied using subperiods defined by the shifts identified in the AI series, calculating differences between the period after the shift and the period before it. Only shifts identified in the AI series were considered, as the aim was to evaluate how PRE and PET respond during intervals when the index itself undergoes significant variations, thereby linking temporal boundaries directly to aridity changes and allowing the PRE and PET contributions to be interpreted as drivers of the observed AI shifts rather than as independent climatic transitions.\u003c/p\u003e \u003cp\u003eMaps were produced showing the relative contribution of PRE and PET and the change in the AI, considering both the climatological subperiods and those defined by the AI shifts. In addition, graphs of AI change and the contributions of PRE and PET were constructed, spatially averaged for the defined regions of the country, applying the same subperiod criteria. The statistical significance of AI changes and the contributions of PRE and PET was assessed using Student\u0026rsquo;s t test at the 95% confidence level.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Spatial Distribution of Linear and Non-linear Trends\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the spatial distribution of linear trends in the AI and its associated climatic variables. These trends are expressed as percentage per decade relative to the temporal mean of the 1961\u0026ndash;2020 period. This approach allows intercomparison among variables and helps identify which one acts as the main driver of aridity changes in each region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe annual AI exhibits both negative and positive trend areas across Argentina (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Negative trends (0\u0026ndash;10% per decade) prevail over much of the territory and are more pronounced in the mountainous regions of the west (35\u0026ndash;40\u0026deg;S). Statistically significant decreases in annual AI are in the Andean region, central Argentina, isolated sectors of the Puna, and the central Patagonian Plateau. In contrast, positive trends (0\u0026ndash;5% per decade) are concentrated in southern Mesopotamia, the southwestern Pampean Plains (37\u0026deg;-41\u0026deg;S and 63\u0026deg;-66\u0026deg;W), and the southernmost part of Patagonia.\u003c/p\u003e \u003cp\u003eAI trends vary seasonally across different regions of Argentina (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-e). In summer, positive trends dominate the north, northwest, and central regions, with significant maxima in the Puna and the Sub-Andean Ranges (10\u0026ndash;20% per decade), while significant negative trends are recorded in the Patagonian and Principal Andes (10\u0026ndash;15% per decade). In autumn, the pattern reverses, with significant increases in the AI in the Principal and Patagonian Andes (10\u0026ndash;15% per decade) and decreases of similar magnitude in the Puna. Winter is characterized by a predominance of negative trends, more intense in central and northern regions (5\u0026ndash;15% per decade, significant in the Sub-Andean Ranges), whereas slight increases (0\u0026ndash;5%) are observed in the Puna, the Frontal Andes, northern Mesopotamia, the central Patagonian Plateau, and some coastal areas. In spring, negative trends are concentrated west of 65\u0026deg;W (0\u0026ndash;15% per decade, significant in the Andes between 30\u0026deg; and 40\u0026deg;S), while positive but non-significant trends (0\u0026ndash;5% per decade) are identified to the east.\u003c/p\u003e \u003cp\u003ePrecipitation exhibits spatial patterns of linear trends like those of AI (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-j). Annual PRE shows a negative trend (0\u0026ndash;5%), more pronounced and significant in the central Andes (5\u0026ndash;10%), while positive but non-significant trends are observed east of the Pampean Plains and in the southernmost part of the country (0\u0026ndash;5%). In summer, increases dominate, especially in the northwest (10\u0026ndash;20%, significant), in contrast to decreases in the Principal and Patagonian Andes (1\u0026ndash;15%, significant). During autumn, the central and northern regions show reductions (5\u0026ndash;15%, significant), whereas the Patagonian Plateau and the Patagonian Andes exhibit increases (5\u0026ndash;15%, significant in the Principal and Patagonian Andes and in the northern plateau). In winter, negative trends prevail, being stronger and significant east of the Sub-Andean Ranges, in central Argentina, and in the northern Chaco Plains (10\u0026ndash;20%), while in some areas slight non-significant precipitation increases (0\u0026ndash;10%) are observed. Finally, in spring, significant increases are identified in the central and eastern regions (0\u0026ndash;5%), whereas the west and south display decreases (5\u0026ndash;15%, significant in the Frontal and Principal Andes).\u003c/p\u003e \u003cp\u003eAnnual PET (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ek) shows a predominance of slight but significant positive trends (0\u0026ndash;1%), although these vary by season. In summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003el), slight PET increases (0\u0026ndash;5%) are observed, significant in central Argentina and Patagonia, whereas in autumn and winter (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003em-n) mixed patterns of increases and decreases occur, without statistical significance. During spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eo), negative trends appear in the south (0\u0026ndash;1%), while positive trends occur in central and northern Argentina (0\u0026ndash;5%), significant north of 35\u0026deg;S.\u003c/p\u003e \u003cp\u003eLinear trend patterns of AI are mainly modulated by precipitation trends. Nevertheless, PET can either moderate or intensify these patterns. For instance, the region exhibiting a negative AI trend is more extensive than that of precipitation alone, because the positive PET trend amplifies the effect of decreasing precipitation, extending arid conditions into areas where precipitation by itself would not show such a pronounced decline.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the spatial distribution of the adjusted coefficient of determination (adj-R\u003csup\u003e2\u003c/sup\u003e) from the third-order polynomial regression for the AI and its associated climatic variables at annual and seasonal scales. The annual AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) shows high and significant adj-R\u003csup\u003e2\u003c/sup\u003e values (0.2\u0026ndash;0.4) in the northwest and in the Principal Andes, indicating good performance of the polynomial fit. In contrast, low or negative values (below 0.1) are observed in the eastern, central, and southern parts of the country, indicating poor nonlinear fit. In summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), the central and northwestern regions stand out with significant values above 0.4, while the rest of the country exhibits low or negative values. In autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), the Puna and the Principal and Patagonian Andes reach high and significant values, unlike the Patagonian Plateau and the central-eastern regions, where lower and non-significant values prevail. In winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), a continuous band from the extreme northwest to Patagonia shows high and significant adj-R\u003csup\u003e2\u003c/sup\u003e values, whereas eastern Argentina and some Andean sectors exhibit low, non-significant values. In spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee), the Puna, northeastern Argentina, and the Frontal and Principal Andes display high and significant values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrecipitation shows adj-R\u003csup\u003e2\u003c/sup\u003e patterns that in several cases resemble those of the AI, although with some differences. At the annual scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), patterns are like those of AI, with significant values in the northwest and in the Principal Andes. In summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg), much of northern and central Argentina, together with a belt over the Patagonian Andes and the southern plateau, exhibits high and significant values above 0.4. In autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh), high values extend over large areas of the country and include sectors of central and northern Patagonia. In winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei), low or negative values dominate, although some central and northern areas east of the Andes show relatively high and significant values (0.25\u0026ndash;0.40). In spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ej), high and significant values are recorded in the Puna, the Andean belt, and northeastern Argentina, while lower values occur in central Argentina, the Pampean region, and Patagonia.\u003c/p\u003e \u003cp\u003eRegarding PET, at the annual scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ek) positive and significant values (greater than 0.2) prevail, with maxima along the Andes between 30\u0026deg; and 40\u0026deg;S and in the eastern Pampean Plains. In summer and spring (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003el and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eo), positive values are again widespread, concentrating in the central-western regions and Patagonia during summer, and in the northwest and northern Chaco Plains during spring. In autumn and winter (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003em and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003en), negative values dominate, indicating that the polynomial model performs worse than an estimate based solely on the mean, and that the nonlinear structure explains only a limited portion of the series variability.\u003c/p\u003e \u003cp\u003eThe nonlinear dynamics of the AI are mainly driven by precipitation, which exhibits the highest and most consistent adj-R\u003csup\u003e2\u003c/sup\u003e values across different regions and seasons. Evapotranspiration also contributes, though more modestly, with significant responses concentrated in the Andean region, the central-west, and the eastern Pampean Plains. Overall, both variables display spatial patterns consistent with those of the AI, particularly in the northwest, the Puna, and the Andes, where the nonlinear signal remains more stable at the annual scale and during summer and autumn.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Long-term Changes in the AI\u003c/h2\u003e \u003cp\u003eLong-term changes in the AI for the 1961\u0026ndash;2020 period were analysed by estimating linear trends (simple regression), non-linear trends (third-order polynomial regression), and climatic shifts (Hubert segmentation) in annually and seasonally averaged time series for the defined regions of the country. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the annual and seasonal AI series for the six regions, together with the linear regression line, the polynomial curve, and the significant shifts detected. In addition, Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show, respectively, the linear trends (B) and the adjusted coefficient of determination (adj-R\u003csup\u003e2\u003c/sup\u003e) values of the annual and seasonal series of AI and its associated climatic variables for each region of the country.\u003c/p\u003e \u003cp\u003e \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\u003eAnnual and seasonal linear trends of the aridity index (AI), precipitation (PRE), and potential evapotranspiration (PET) for the different regions of the country. Values are expressed as the percentage of the slope relative to the mean of the 1961\u0026ndash;2020 period per decade. Asterisks indicate statistically significant linear trends, assessed using the Student\u0026rsquo;s t-test at the 95% confidence level (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e 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char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.68*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-10.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e 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colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.51*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-10.98*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-9.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.17*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTransition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.98*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.78*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.12*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.36*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.88*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.45*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.92*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.71*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.53*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExtra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.25*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.76*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.11\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=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted coefficient of determination (adj-R\u003csup\u003e2\u003c/sup\u003e) of the third-order polynomial model for the annual and seasonal aridity index (AI), precipitation (PRE), and potential evapotranspiration (PET) for the different regions of the country. Asterisks indicate statistically significant polynomial models, assessed using the Fisher test at the 95% confidence level (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSubandes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.40*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.60*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTransition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.06*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.13*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.42*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.24*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eExtra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.05\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 annual AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) exhibits a negative linear trend in all regions, being statistically significant in Andean and Extra-Andean Patagonia, with decreases of 2.88% and 2.25% per decade, respectively. The East region shows a smaller, non-significant reduction in the AI (0.45% per decade), likely influenced by subregional areas with positive trends that dampen the regional mean. A similar pattern is observed in the Transition region, where the non-significant negative trend (1.56% per decade) is modulated by areas with opposing signals within the region. Non-linear trends in annual AI show, for the Northwest, Subandes, Transition, and Extra-Andean Patagonia, a rapid increase until the early 1980s, followed by a sustained and significant decrease, whereas in Andean Patagonia and the East region a polynomial fit like the linear trend is observed.\u003c/p\u003e \u003cp\u003eAt the seasonal scale, regions show coherent patterns. In summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), positive trends dominate and are significant in the Northwest, Subandes, and Transition regions (6.98\u0026ndash;15.51% per decade), while Andean Patagonia exhibits a non-significant negative trend. In autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec), positive trends occur in the Transition, East, and Patagonian regions (both Andean and Extra-Andean), being significant in the latter two and with the largest increase in Andean Patagonia (9.45% per decade), whereas non-significant negative trends are found in the Northwest and Subandes regions. In winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed), a negative trend is observed nationwide, although it is statistically significant only in the Transition region (7.06% per decade). In spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee), the index decreases in the Northwest and Patagonia (Andean and Extra-Andean) and increases in the rest of the country, without statistical significance.\u003c/p\u003e \u003cp\u003eSeasonal non-linear trends exhibit low-frequency oscillations superimposed on linear changes. In summer, a decrease prior to 1990 is followed by a marked increase after that decade, especially in the Northwest, Subandes, Transition, and East regions. In autumn, a progressive decline until the late 1990s is detected, followed by a rapid increase in the northern and central regions, while variations in the Patagonian regions are more moderate. In winter, a rapid decrease until the mid-1970s dominates, followed by a sustained increase since 1990, significant in the Subandes, East, and Extra-Andean Patagonia regions. In spring, variations are less pronounced, pointing to a mainly linear behaviour.\u003c/p\u003e \u003cp\u003eAnnual and seasonal AI series exhibit significant climatic shifts according to the Hubert segmentation. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the shifts identified in the AI series and its associated climatic variables, their main characteristics, the year of occurrence, and the affected regions. For the annual AI, a negative shift is observed in 1997/1998 in the Northwest, Subandes, Transition, and Extra-Andean Patagonia regions (10\u0026ndash;14% decrease), and a later negative shift in 2005/2006 in Andean Patagonia. In summer, a positive shift occurs in 1985/1987 in the Northwest and Subandes regions (70\u0026ndash;83% increase), followed by another positive shift in 1998/2000 in the Transition and East regions (25\u0026ndash;38% increase). In autumn, a negative shift is recorded in 1972/1973 in the Northwest and Subandes regions (26\u0026ndash;33% decrease), while in 1980/1981 a positive shift occurs in Andean Patagonia (57% increase). Finally, in spring, a single negative shift is detected in 1974/1975 in the Northwest region, with an average decrease of 29%. No significant shifts are identified in winter using the applied method.\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\u003eCharacterization of climatic shifts identified in the annual and seasonal aridity index (AI), precipitation (PRE) and potential evapotranspiration (PET) series\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of shift\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTiming of shift\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegions of occurrence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApproximate change (Δ%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eAnnual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1997/1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition, Extra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2005/2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1997/1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u0026ndash;12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2001/2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubandes, Andean Patagonia, Extra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;12%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1976/1977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast, Andean Patagonia, Extra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u0026ndash;2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1985/1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest\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=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2002/2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubandes, Transition, Este, Andean Patagonia, Extra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u0026ndash;3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1985/1987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70\u0026ndash;83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1998/2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTransition, East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u0026ndash;38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1985/1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u0026ndash;49%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1996/1997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition, East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21\u0026ndash;32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1977/1978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndean Patagonia, Extra-Andean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026ndash;5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2006/2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition, East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u0026ndash;5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1972/1973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26\u0026ndash;33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1980/1981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1979/1980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24\u0026ndash;40%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1980/1981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2008/2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAndean Patagonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4%\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\u003ePRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1982/1983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSubandes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1974/1975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1993/1994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2001/2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNorthwest, Subandes, Transition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u0026ndash;6%\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Temporal Variability of PRE and PET\u003c/h2\u003e \u003cp\u003eSince the AI depends on the combined behaviour of precipitation (PRE) and potential evapotranspiration (PET), their temporal variability over the 1961\u0026ndash;2020 period was analysed for each region of Argentina. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the annual and seasonal PRE and PET series spatially averaged for the six regions, together with the linear regression, the polynomial curve, and the identified climatic shifts. At both annual and seasonal scales, both variables exhibit marked interannual variability, although common temporal patterns can be identified across regions. In addition, Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present, respectively, the linear trends (B) and the adjusted coefficients of determination (adj-R\u0026sup2;) of the annual and seasonal PRE and PET series for each region of the country, allowing an assessment of the consistency between the trends of these variables and those of the AI. Likewise, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e summarizes the shifts identified in the PRE and PET series, together with their main characteristics, facilitating the evaluation of whether the discontinuities in the AI were associated with variations in one or both variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the annual scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), precipitation shows a negative linear trend in nearly all regions, though only significant in Andean Patagonia (-2.48% per decade), while a slight positive trend is observed in the East region. In most regions, and in agreement with the AI, PRE displays a rapid increase until the early 1980s followed by a sustained decline to the present. In addition, negative shifts are detected in 1997/1998 in the Northwest and Transition regions and in 2005/2006 in Andean Patagonia, all coincident with negative shifts in the annual AI. Shifts between 2001 and 2006 are also identified in the Subandes and Extra-Andean Patagonia regions, which are not reflected in the annual AI. Regarding annual PET, a significant increase is observed in all regions, with changes less pronounced than those of PRE, except in the East region, where the PET increase (0.73%) exceeds the slight PRE decrease (0.02%). In the Subandes and Transition regions, PET shows a decrease until the late 1970s and early 1980s, followed by a sustained increase, whereas in the other regions the polynomial fit is very similar to the linear regression. Finally, PET records positive shifts at different times, none of which coincide with those observed in the AI for the corresponding regions.\u003c/p\u003e \u003cp\u003eIn summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), changes in PRE more closely track variations in the AI. PRE increases in most regions, consistent with the positive AI trend, except in Andean Patagonia, where precipitation declines together with a reduction in the index. In the Transition and East, PRE decreases until the 1970s, followed by an increase until around 2010 and a subsequent decline, whereas in the Northwest, Subandes, and Andean Patagonia the polynomial fits closely resemble the linear trends. In Extra-Andean Patagonia, PRE increases until the 2000s and then decreases rapidly. PRE exhibits positive shifts in 1985/1986 and 1995/1997 in the Northwest, Subandes, Transition, and East regions, possibly associated with the positive AI shifts observed in the same periods for the Transition and East regions. In contrast, PET exhibits a positive trend and poorly defined polynomial fits in all regions, except in the Subandes and Transition regions, where it decreases slightly until the late 1980s and early 1990s and then increases toward the present. PET also shows two positive shifts (one in 1977/1978 in Andean and Extra-Andean Patagonia and another in 2006/2012 in the Northwest, Subandes, Transition, and East regions) that do not coincide with AI shifts in this season.\u003c/p\u003e \u003cp\u003eIn autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec), PRE trends follow changes in the AI in the Northwest, Subandes, and Patagonian regions (Andean and Extra-Andean), with decreases in the first two and increases in the latter two. In the Transition and East regions, PET increases while PRE decreases; therefore, the signs of the trends do not match, and the increase in the AI cannot be attributed to precipitation alone. Regarding the non-linear behaviour of PRE, the Northwest, Subandes, Transition, and East regions show a rapid decrease until the late 1980s, followed by a stable period until the early 2000s and a slight rebound thereafter, whereas Andean and Extra-Andean Patagonia exhibit a decrease during the 1960s followed by a rapid increase until the late 2000s and a subsequent decline to the present. PRE shows a positive shift in 1980/1981 in Andean Patagonia, coincident with an autumn AI shift in the same period. PET does not exhibit clear trends in most regions, except in Andean and Extra-Andean Patagonia, where a positive linear trend is observed together with a slight decrease until the late 1990s, followed by a rapid increase toward the present. PET also presents a positive shift in 2008/2009 in Andean Patagonia that is not reflected in the AI.\u003c/p\u003e \u003cp\u003eIn winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), most regions show a decrease in PRE consistent with the negative AI trends. In Andean Patagonia and the Northwest, positive trends in both PRE and PET are observed, though with different effects: in Andean Patagonia, the positive PET trend exceeds that of PRE and leads to a decrease in the AI, whereas in the Northwest both trends are non-significant and fail to explain the AI change. Polynomial trends in PRE differ among regions: the Northwest and Andean Patagonia show a decrease until the late 1970s followed by an increase until the mid-2000s and a subsequent decline; the Subandes and Extra-Andean Patagonia regions show a rapid decrease until the early 1990s and a slight increase thereafter; and the Transition and East regions show a polynomial fit similar to the linear one. PET generally exhibits weak linear trends and polynomial fits that are like the linear trend, except in the Northwest, where a slight decrease is observed since the early 2000s. No significant winter shifts are detected in PRE or PET in most regions, consistent with the absence of AI shifts, with the sole exception of a negative shift in the Subandes region in 1982/1983 that is not reflected in the index.\u003c/p\u003e \u003cp\u003eIn spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee), PRE trends again follow AI trends in most regions, with negative values in the Northwest and Patagonia (Andean and Extra-Andean) and positive values in the East and Transition regions. In the Subandes, however, a negative PRE trend and a positive PET trend are observed, which do not align with the non-significant positive AI trend and indicate a decoupling between the climatic variables and the index. In terms of non-linear behaviour, PRE shows marked regional differences: the Northwest, Subandes, Transition, and East regions experience a rapid decrease until the mid-1970s, followed by an increase until the mid-2000s and a new decline toward the present; Andean Patagonia shows an increase until the early 1980s followed by a sustained decrease; and Extra-Andean Patagonia shows a polynomial fit similar to the linear trend. PET maintains a positive trend in most regions. In Subandes, Transition, and East, a decrease is observed until the late 1970s, followed by a progressive increase toward the present, whereas in the Northwest and in Patagonia (Andean and Extra-Andean) the polynomial fit is very similar to the linear trend. In addition, PRE shows no shifts in any region in spring, while PET exhibits two positive shifts in 1993/1994 and 2001/2002 that do not coincide with the AI shift in 1974/1975 in the Northwest, indicating that the latter cannot be attributed to either PRE or PET.\u003c/p\u003e \u003cp\u003eThe relationship between the annual and seasonal linear PRE and PET trends and the AI trends (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) shows differences across the regions of the country. In the Northwest, the annual mean and the transitional seasons (autumn and spring) show that the negative AI trend results from decreasing PRE combined with increasing PET, whereas in summer the simultaneous increase of both variables leads to an increase in the AI, and in winter the non-significant trends in PRE and PET (3.31% and 0.15%) do not explain the observed AI change. In the Subandes region, annual, summer, and autumn trends replicate the Northwest pattern, while in winter decreasing precipitation reduces the index and in spring a nearly neutral AI is linked to slightly negative PRE offset by positive PET. In the Transition region, the annual mean and summer, winter, and spring seasons show changes like those of the previous regions, with AI and PRE increases in summer and decreases in the annual mean, winter, and spring, while in autumn a slight AI increase may be related to marginally negative PRE compensated by positive PET. In the East, the nearly neutral annual AI trend is associated with trendless PRE and markedly positive PET, while in summer and spring AI exhibits a positive trend associated with positive trends in PRE and PET; in winter, AI decreases mainly due to the negative trend in PRE, and in autumn its positive trend is not directly explained by either a decrease in PRE or an increase in PET. In Andean Patagonia, annual, summer, and spring AI decrease due to negative PRE trends, autumn AI increases in response to a positive PRE trend, and winter AI decreases because PET shows a more pronounced positive trend than PRE. In Extra-Andean Patagonia, annual and seasonal AI trends are primarily controlled by precipitation, with decreases in the annual mean, winter, and spring, and increases in summer and autumn.\u003c/p\u003e \u003cp\u003eThe adjusted coefficients of determination (adj-R\u003csup\u003e2\u003c/sup\u003e) of the polynomial models for the annual and seasonal PRE and PET (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) series are associated with the adj-R\u003csup\u003e2\u003c/sup\u003e values of the AI in different ways across the regions of the country. In the Northwest, AI shows the best polynomial fit in summer, mainly modulated by PRE, whereas in the annual mean and the other seasons AI structures are weaker due to the low explanatory power of PRE and PET, especially in winter. In the Subandes region, AI exhibits a robust fit in summer supported by a strong non-linear structure in PRE and a smaller contribution from PET; in autumn, the low adj-R\u003csup\u003e2\u003c/sup\u003e of AI (0.08) does not reflect the coherent structure of PRE with a high adj-R\u003csup\u003e2\u003c/sup\u003e (0.36); and in the annual mean, winter, and spring, fits are weak and only partially explained by PRE or PET. In the Transition region, polynomial models of AI again stand out in summer with PRE as the dominant signal, whereas in the annual mean and other seasons the fits are smaller or weak for both AI and PRE and PET. In the East region, AI shows weak fits at both annual and seasonal scales, while a well-defined fit is observed in annual PET (adj-R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.31) and in summer PRE (adj-R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17), indicating that the non-linear behaviour of the variables is not reflected in the AI changes. In Andean Patagonia, AI shows consistent non-linear modelling at annual and autumn scales in agreement with PRE and reinforced by PET, whereas all three variables exhibit poor fits in summer, winter, and spring. In Extra-Andean Patagonia, AI maintains low fits, with only autumn showing a significant model aligned with PRE, while PET exhibits a more pronounced structure at annual and summer scales that is not reflected in the AI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Climatological Changes in the AI and Contribution of PRE and PET\u003c/h2\u003e \u003cp\u003eThe changes in the annual and seasonal AI between 1961\u0026ndash;1990 and 1991\u0026ndash;2020 were analysed, and the contributions of precipitation and PET to these changes were quantified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This approach allows identification, from a climatological perspective, of the specific role of each variable in the AI variations at both annual and seasonal scales. The comparison between these two 30-year periods provides a representation of the mean change, capturing long-term trends and structural differences, which facilitates the description of an average climate change with adequate statistical stability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe annual AI (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea) shows a decrease over most of the country, more pronounced and significant in the Principal and Patagonian Andes between 34\u0026deg; and 45\u0026deg;S, while some areas\u0026mdash;such as southwestern Buenos Aires (around 40\u0026deg;S and 64\u0026deg;W), southern Mesopotamia, and southern Patagonia\u0026mdash;exhibit non-significant increases. These patterns vary by season: in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb), increases dominate in the centre and north (significant in the central-northwestern region), contrasting with significant decreases in Patagonia and the Andes; in autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), the pattern reverses, with strong decreases in the centre and north and significant increases in Patagonia and the Andes south of 30\u0026deg;S; during winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed), negative changes prevail (more intense in Patagonia), although areas of index increase appear east of the Sub-Andean Ranges, the Puna, and the Frontal Andes; in spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee), non-significant positive changes are observed over the Chaco-Pampean Plains (east of 67\u0026deg;W), while significant decreases dominate in the Andes Mountain Range and Patagonia (between 30\u0026deg; and 45\u0026deg;S).\u003c/p\u003e \u003cp\u003eThe contributions of annual and seasonal precipitation (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef-j) display spatial patterns very similar to those of the AI, though with slightly more extensive significant areas. For example, in summer, significant positive contributions extend from the northwest and central regions to the Atlantic coast. Likewise, in spring, the significant decrease over the Andes reaches parts of the Patagonian Plateau. This suggests that precipitation modulates the spatial structures of AI change, although in some regions these patterns may be strengthened or weakened by the effect of PET.\u003c/p\u003e \u003cp\u003eOverall, PET shows more moderate contributions than precipitation. At the annual scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ek), significant increases are recorded over much of Argentina, more pronounced in the Andes between 33\u0026deg; and 50\u0026deg;S. At the seasonal scale, positive changes dominate, with differences among seasons: in summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003el), increases are significant only in a belt north of the Patagonian Plateau; in autumn and winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003em-n), most of the territory shows no significant changes, although in winter very strong increases occur along the Andes Mountain Range, exceeding 30% in the Frontal, Principal, and Patagonian Andes; in spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eo), central and northern Argentina show positive values, significant in Mesopotamia, the Chaco Plains, and the Sub-Andean Ranges up to the Puna. This suggests that evaporative demand plays a secondary role in the AI changes; however, by contributing positively at both annual and seasonal scales, it can intensify AI change patterns (as in the Patagonian Andes during summer) or attenuate them (as in northeastern Argentina during spring).\u003c/p\u003e \u003cp\u003eFor a more detailed analysis, changes in the AI and the contributions of PRE and PET were calculated at annual and seasonal scales, spatially averaged for each region of the country, by comparing the mean values of the 1961\u0026ndash;1990 and 1991\u0026ndash;2020 periods. At the annual scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea), a negative change in the AI is observed in all regions, with the largest decrease in Andean Patagonia (7.9%). PET shows significant increases in all cases (0.4\u0026ndash;2.3%), while precipitation exhibits non-significant decreases in most regions (1.1\u0026ndash;6.3%), except for a slight increase in the East (0.7%). In this latter region, the change in the AI is driven by the more pronounced increase in PET (2.3%), which exceeds the contribution of precipitation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring summer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb), changes in the AI are mainly controlled by precipitation, as PET shows small and non-significant increases in most regions. In the Northwest, Subandes, Transition, and East regions, increases in the index are associated with significant increases in precipitation (10.9\u0026ndash;39.3%), while in Extra-Andean Patagonia this increase is much smaller (0.4%). In Andean Patagonia, the index decreases by 7.9% due to a reduction in precipitation combined with a significant increase in PET (0.5%). In autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), the Northwest, Subandes, and Transition regions exhibit decreases in the index linked to a significant reduction in precipitation (11.5\u0026ndash;26.5%), while in the East this decrease is more moderate (3.7%). In contrast, Andean and Extra-Andean Patagonia show significant increases in the AI driven by increased precipitation, with Andean Patagonia standing out due to the strong contribution of this variable (37.6%). PET plays a secondary role in this season, with positive contributions ranging from 0.6 to 3.5%. In winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), most regions experience a reduction in the AI, which is significant in the Subandes and Transition regions. Andean Patagonia represents a particular case, as PET contributes more than precipitation (49.5% versus 30.2%), resulting in a marked decrease in the index (19.3%). By contrast, the Northwest shows an increase in the AI (8.5%) associated with an increase in precipitation (16.3%) that is not fully offset by the positive contribution of PET (7.8%). Finally, spring (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee) is characterized by index changes once again dominated by precipitation, with a mixed pattern of increases (Subandes, Transition, and East) and decreases (Northwest, Andean and Extra-Andean Patagonia), while PET maintains a secondary role with positive contributions between 0 and 4.8%. Andean Patagonia again shows the largest decrease in the index (22.3%), explained by a pronounced decline in precipitation (21.2%) together with a slight increase in PET (1.1%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Climatic Shifts in the AI and Contribution of PRE and PET\u003c/h2\u003e \u003cp\u003eChanges in the AI and the contributions of precipitation and PET were also analysed by comparing periods defined by the climatic shifts of the AI itself (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Unlike analyses based on fixed climatic periods, where long-term averages may mask abrupt transitions, the use of variable periods allows identification of why the AI changes at specific times, since the temporal boundaries emerge from its own dynamics. This approach reveals how precipitation and PET respond during intervals when the index undergoes significant variations, facilitating the identification of their dynamic changes and the processes that accompany them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the annual AI shifts, opposite and significant contributions are identified: negative precipitation and positive PET strongly reduce the AI. The negative shift of 1997/1998, detected in the Northwest, Subandes, Transition, and Extra-Andean Patagonia regions, shows an extensive area of significant decrease from the northwest to Patagonia, associated with the combination of negative precipitation and positive PET, both significant (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eh, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eo). The 2005/2006 shift in the Andean Patagonia region exhibits a significant decrease in the index and precipitation in the Principal and Patagonian Andes, while PET in the same region shows a slight but significant increase (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ei, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ep).\u003c/p\u003e \u003cp\u003eFor summer AI shifts, the significant increase in the index is mainly related to a positive and significant precipitation contribution, as PET shows only slight positive contributions across most of the country. In the positive shift of 1975/1987, recorded in the Northwest and Subandes regions, the index increase coincides with significant positive contributions from both variables, though more pronounced for precipitation (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ej, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eq). The positive shift of 1998/2000 in the Transition and Eastern regions shows the same pattern, with a significant increase in precipitation and a slight, non-significant increase in PET. In this sense, although PET shows significant changes only in Patagonia during summer, it neither determines nor contributes significantly to summer AI shifts (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ed, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ek, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003er).\u003c/p\u003e \u003cp\u003eFor autumn AI shifts, the pattern observed in summer is repeated: precipitation controls the index change, while PET contributes very little. The negative shift of 1972/1973 in the Northwest and Subandes regions shows a broad area of significant index decrease over central and northern Argentina, consistent with a significant decline in precipitation and a slight, non-significant decrease in PET in those regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ee, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003el, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003es). The positive shift of 1981/1982 in Andean Patagonia shows a significant index increase explained by the positive contribution of precipitation, while PET displays slightly positive contributions in the northern part of the region (30\u0026deg;-40\u0026deg;S) and negative contributions in the south (40\u0026deg;-50\u0026deg;S) (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ef, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003em, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003et). Together, these results confirm that, as in summer, autumn index changes depend on precipitation, since evaporative demand shows only weak and non-significant contributions.\u003c/p\u003e \u003cp\u003eFor the spring AI shift, changes are more moderate than in summer and autumn. In the Northwest, where the negative shift of 1974/1975 was recorded, the most pronounced and widespread index decrease across much of the country is identified, associated with a significant negative contribution from precipitation, especially in the Frontal and Principal Andes, while PET shows a slight, non-significant increase (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eg, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003en, \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eu). This pattern is like that of the annual AI shifts, where the combination of negative precipitation and positive PET determines the index decrease.\u003c/p\u003e \u003cp\u003eThe contributions of PRE and PET to the total change in AI were quantified at annual and seasonal scales, spatially averaged for each region of the country, by comparing the mean values of the periods defined by the climatic shifts of the index itself (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). When comparing the periods defined by the climatic shifts detected in the annual AI series (1997/1998 and 2005/2006), a decrease in the AI is observed in all regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). This reduction is statistically significant in most cases and is mainly explained by negative contributions from precipitation accompanied by small positive contributions from PET. Andean Patagonia stands out as the region with the largest decreases in both shifts, with significant changes of 10.6% in the first and 17% in the second. In the East, a reduction in the AI is also observed, associated with a non-significant decrease in precipitation and a significant increase in PET, whose contribution in the 2005/2006 shift (3.4%) exceeds that of precipitation (0.9%), resulting in a negative index change of 2.5%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the shifts identified in the summer series (1985/1987 and 1998/2000), significant increases in the AI are observed in the Northwest, Subandes, Transition, and East regions (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed), related to marked increases in precipitation and a secondary role of PET. The Subandes concentrate the largest magnitudes of change in both shifts (41.7% in 1985/1987 and 36.9% in 1998/2000), with significant precipitation increases between 11% and 18%. In contrast, Andean and Extra-Andean Patagonia show minimal changes in both cases, well below those recorded in the rest of the country.\u003c/p\u003e \u003cp\u003eThe shifts in the autumn series (1972/1973 and 1980/1981) show an AI decrease in the Northwest, Subandes, Transition, and East regions, explained by strong negative contributions from precipitation (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ee and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ef). The opposite occurs in Andean and Extra-Andean Patagonia, where the index increases due to positive precipitation contributions, more pronounced in Andean Patagonia (28% in 1972/1973 and 42% in 1980/1981). PET maintains a secondary role in both shifts, with small variations compared to those of precipitation, although the East region presents the highest values (4.1% and 3% in each shift).\u003c/p\u003e \u003cp\u003eThe spring shift recorded in 1974/1975 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eg) is characterized by a decrease in the AI in most regions. The Northwest stands out as the only region where this reduction is significant (10.7%), associated with a decline in precipitation (9.1%) together with a slight increase in PET (1.6%). In contrast, the East shows an increase in the index because of an increase in precipitation (5.4%) that offsets the slight negative contribution of PET (0.01%).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION AND CONCLUSIONS","content":"\u003cp\u003eThe AI evolution in Argentina during the 1961\u0026ndash;2020 period reflects an integrated response to changes in water availability and atmospheric evaporative demand. In this context, precipitation emerges as the primary driver of the spatial and temporal signals of the index, while PET modulates their intensity and extent. This behaviour is consistent with global studies showing that precipitation explains most of aridity variability, whereas warming has steadily increased the relative contribution of PET (Yang et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Along these lines, AI linear trends are largely determined by precipitation trends, which define both the sign and the spatial structure of the changes, while increases in PET intensify aridification signals and promote the expansion of affected areas. This pattern is consistent with the global expansion of arid and semiarid regions documented in recent decades, attributed to rising PET and heterogeneous changes in precipitation (Feng and Fu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In turn, AI non-linear trends reveal a dynamic dominated by precipitation variability associated with low-frequency oscillations, together with a more localized and seasonal contribution of PET, which either reinforces or attenuates these signals depending on the region. Similar results were reported for China, where increases in aridity were linked to interdecadal transitions in temperature and precipitation (Li et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Overall, these findings indicate that aridity responds to complex climatic dynamics that cannot be interpreted solely through linear trends.\u003c/p\u003e \u003cp\u003eAt the national scale, AI changes in Argentina are mainly controlled by precipitation, which regulates the sign, magnitude, and spatial structure of its variations, both when considering 30-year mean values and periods defined by climatic shifts. Under both approaches, precipitation stands out as the primary explanatory factor of the spatio-temporal pattern of aridity, indicating that transitions in the aridity regime are associated with persistent or abrupt changes in water availability. This behaviour is consistent with studies conducted in different regions of Argentina, which documented a transient reduction in aridity in the central part of the country during the twentieth century, linked to increased precipitation and accompanied by greater seasonality and spatial concentration (Rusticucci and Penalba, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Minetti et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Penalba and Vargas, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; De la Casa and Ovando, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Barros et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Doyle, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In contrast, PET shows more moderate and spatially limited contributions, with a predominantly positive signal that modulates the intensity of index changes by amplifying decreases when it coincides with reductions in precipitation or attenuating them when both variables act in opposite directions. This result is comparable with previous studies indicating that warming and the intensification of thermal extremes have increased atmospheric water demand, particularly in the Pampas and Patagonia regions (Ferrelli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; M\u0026uuml;ller et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brendel et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Barros et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), such that even in areas with higher precipitation, aridity may intensify due to increasing PET and the recurrence of dry periods (Llano and Penalba, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rivera et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Panza et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, the analysis based on fixed 30-year periods allowed the characterization of the mean change in the index under a stable climatological framework, whereas periods defined by climatic shifts revealed the internal dynamics of these changes, showing that the most intense aridification episodes occur when decreases in precipitation are combined with increases in evaporative demand. Overall, the consistency between both approaches reinforces the need to interpret aridity from an integrated water-balance perspective, in which precipitation constitutes the main water input and PET acts as a key modulating factor of changes in the index.\u003c/p\u003e \u003cp\u003eIn Argentina, long-term changes in aridity result from the combination of linear trends, nonlinear dynamics, and climatic shifts. Although the AI shows a generalized process of aridification at the national scale, in line with what was reported by Blanco and Doyle (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and with global studies documenting an aridity intensification in both drylands and humid regions (Daramola and Xu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the regional and seasonal analysis reveals contrasts in the sign, intensity, timing, and climatic factors driving these changes. Thus, the northern and central regions of the country exhibit more complex and variable temporal patterns, with more pronounced changes in summer and autumn, whereas Patagonia shows more gradual variations, dominated by linear trends and low-frequency oscillations. In most regions and seasons, AI changes are mainly controlled by precipitation, which governs both trends and abrupt shifts; however, in central and eastern sectors of the country, particularly in autumn and winter, PET plays a more relevant modulating role, amplifying or attenuating the index signals depending on its interaction with precipitation. This behaviour is consistent with previous evidence indicating that increases in PET contribute to the intensification of global droughts, while their distribution and variability are modulated by precipitation (Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Overall, these results show that national averages tend to obscure key processes, especially in countries with pronounced topographic and climatic diversity such as Argentina (Beck et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pereyra et al., 2019) and reinforce the need to address aridity through an integrated regional analysis of the water balance.\u003c/p\u003e \u003cp\u003eThe contributions of precipitation and PET to the AI change in the different regions of the country were evaluated, comparing both the averages of fixed 30-year periods and those defined by climatic shifts. In the Northwest, Subandes, and Transition regions, AI variations are dominated by precipitation, indicating that aridity responds mainly to changes in water supply, while evaporative demand plays a secondary role. In the East region, by contrast, PET determines the annual aridification signal and reinforces decreases in the index during abrupt changes, acting as a key forcing even when precipitation variations are weak or not significant. This pattern agrees with the observations of Ruscica et al. (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), who associated the increase in evapotranspiration in southeastern South America with local changes in land cover, highlighting the importance of evaporative demand as a modulator of regional aridity. In Extra-Andean Patagonia, AI changes are small, with moderate contributions from precipitation and a marginal role of PET, whereas in Andean Patagonia the decrease in precipitation and the significant increase in PET lead to increases in aridity, reflecting an efficient coupling between water and energy forcings. These findings are consistent with studies documenting the intensification of negative precipitation trends in the Patagonian Andes (Martin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oruezabal et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the recent transition toward warmer and drier conditions, with impacts on glaciers and river flows (Rivera et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hurtado et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ricetti et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, the results show that precipitation governs the direction and spatial structure of aridity changes in Argentina, while PET acts as a regional amplifying factor capable of intensifying aridification during climate transitions.\u003c/p\u003e \u003cp\u003eThis study provides detailed evidence on the climatic factors controlling aridification in Argentina by offering an explicit and regionally differentiated quantification of the contributions of precipitation and PET to changes in aridity, an aspect that has been little explored in the literature, especially in the Southern Hemisphere (Ma and Fu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zarch et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By integrating linear trends, nonlinear dynamics, and climatic shifts within a single analysis, the traditional approach based solely on long-term averages is surpassed, demonstrating that aridification can intensify abruptly when changes in water supply and atmospheric demand converge. This has direct implications for assessing climate vulnerability, as it allows the identification of regions where aridification is dominated by precipitation deficits and others where increasing evaporative demand is a critical factor, which is essential for guiding adaptation and mitigation strategies. Identifying the climatic factors that control aridity provides a solid scientific basis for the sustainable management of water resources in areas vulnerable to desertification, where small changes in the water balance can cause disproportionate impacts on ecosystems and human activities (Berdugo et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the context of global climate change, our results offer a more accurate view of aridification, which can help improve environmental and living conditions for populations affected by increasing water scarcity, as in Andean Patagonia (Brendel et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and mitigate the effects of adverse processes such as soil degradation and desertification in central Argentina (Torres et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Civeira and Rodriguez, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution statement\u003c/h2\u003e\n\u003cp\u003ePedro Samuel Blanco carried out the research, produced the figures, and wrote the manuscript. Moira Evelina Doyle performed a critical review to ensure the intellectual rigor of the content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Scientific and Technical Research Council of Argentina (PIP KE2 11220210100752CO).\u003c/p\u003e\n\u003ch2\u003eConflict of Interest statement\u003c/h2\u003e\n\u003cp\u003eThe authors reported no potential conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003ePedro Samuel Blanco carried out the research, produced the figures, and wrote the manuscript. Moira Evelina Doyle performed a critical review to ensure the intellectual rigor of the content.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThe authors express their gratitude for the comments provided by the editors and reviewers, which have been valuable in improving the quality of the research.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe gridded dataset used in this study is publicly accessible and can be obtained from the following link: CRU TS 4.06 - [https://crudata.uea.ac.uk/cru/data/hrg/cru\\_ts\\_4.06/](https:/crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.06) (Harris et al. 2020).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarros VR, Boninsegna JA, Camilloni IA, Chidiak M, Magr\u0026iacute;n GO and Rusticucci M (2015) Climate change in Argentina: trends, projections, impacts and adaptation. \u003cem\u003eWIREs Climate Change \u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 151-169. http://dx.doi.org/10.1002/wcc.316 \u003c/li\u003e\n\u003cli\u003eBeck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A and Wood EF (2018) Presentand future K\u0026ouml;ppen-Geiger climate classification maps at 1-km resolution. \u003cem\u003eScientific Data\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 1-12. https://doi.org/10.1038/sdata.2018.214 \u003c/li\u003e\n\u003cli\u003eBenjamini Y and Braun H (2002) John W. 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This study analyses precipitation (PRE) and potential evapotranspiration (PET) contributions to aridity changes across Argentina during 1961\u0026ndash;2020. Using monthly precipitation and mean temperature data from the Climatic Research Unit (CRU), the United Nations Environment Programme (UNEP) aridity index (AI) was calculated at annual and seasonal scales. Subsequently, linear and nonlinear trends, climatic shifts, and PRE and PET contributions to AI changes were evaluated for six regions using fixed 30-year periods and pre- and post-shift means. The results show a widespread decrease in annual AI across much of Argentina (1\u0026ndash;10% per decade), driven primarily by PRE reductions and, to a lesser extent, by PET increases. Seasonally, AI increases prevail in summer and autumn (locally\u0026thinsp;\u0026gt;\u0026thinsp;30% per decade), decreases in winter (1\u0026ndash;30% per decade), and a mixed pattern emerges in spring (-20 to 10% per decade), with PRE as the dominant control. Nonlinear variability in the AI is controlled by PRE, with the strongest declines observed in the Andean and Extra-Andean Patagonia (-2.9 and \u0026minus;\u0026thinsp;2.3% per decade), associated with PRE decreases and PET increases. In contrast, the Northwest and Subandes regions show the greatest seasonal variability, with summer AI increases (7\u0026ndash;16% per decade) and abrupt shifts (70\u0026ndash;83%), whereas in East PET increases (\u0026gt;\u0026thinsp;3.4%) exceed the PRE contribution and account for more moderate AI declines (-2.5%). Overall, aridification in Argentina is primarily governed by PRE changes, while PET acts as a regional and seasonal amplifier, highlighting the need for an integrated water-balance approach to assess aridity and its impacts.\u003c/p\u003e","manuscriptTitle":"Contribution of Precipitation and Potential Evapotranspiration to Long-Term Changes in Aridity in Argentina Over Recent Decades","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:14:22","doi":"10.21203/rs.3.rs-8781542/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-24T14:41:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-10T13:01:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185957618477036504514560734076152259729","date":"2026-02-20T03:48:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67763652084375921606213915930723572171","date":"2026-02-10T01:10:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-09T01:20:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T05:04:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T05:03:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2026-02-04T03:53:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f455213c-bb02-433d-a0f4-a0f9e46af5c9","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T14:55:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 12:14:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8781542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8781542","identity":"rs-8781542","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}