Spatiotemporal dynamics of the aridity index in Xinjiang over the past 60 years

Spatiotemporal dynamics of the aridity index in Xinjiang over the past 60 years | 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 Spatiotemporal dynamics of the aridity index in Xinjiang over the past 60 years Xiulan Wu, Cunjie Zhang, Siyan Dong, Jiahui Hu, Xinyi Tong, Xiannian Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2689317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Aug, 2023 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Precipitation change and dry‒wet trend are the most critical natural factors that determine the social development and civilization process in Xinjiang. The Aridity Index (AI), calculated from 99 homogeneous meteorological stations from 1961 to 2020,was used to analyze the variation of dry‒wet climate change in Xinjiang in the past 60 years . The results show that the annual AI in Xinjiang has shown a significant decreasing trend over the past 60 years; that is, the climate in Xinjiang, especially southern Xinjiang, has become wetter. The interdecadal variations from the 1960s-2010s show that the total station ratio of arid and extremely arid areas in Xinjiang showed a decadal decreasing trend, while the subarid areas and humid and semihumid areas showed increasing trends, especially since the beginning of the 21st century. The interdecadal spatial evolution characteristics show that the dry‒wet climate in Xinjiang reversed in the 1990s. An abrupt change in the annual aridity index occurred in 1986, after which the study region was basically in a continuous wetting process. The first empirical orthogonal function (EOF) decomposition mode is consistent in Xinjiang; that is, the climate in Xinjiang is generally dry or wet, and the intensity of this change varies among different regions. The second mode reflects the opposite spatial distribution characteristics of the dry‒wet climate in southern and northern Xinjiang with the Tianshan Mountains. Dry‒wet climate changes in Xinjiang mainly exhibit 2.5-year and 6-year oscillation periods, between which the 6-year period is more significant. Dry‒wet climate change Aridity index Observations Xinjiang Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction In the past few decades, climate change has had increasingly significant impacts on the global and regional water cycles and the distribution of dry‒wet climate zones.The arid region of central Asia is the largest non-zonal arid region in the world. Its ecological environment is fragile and sensitive to global climate change(Chen et al.2011;Donat et al.2019;Ma et al.2019).The arid region of Central Asia responded significantly to the second global warming in the 20 th century, and the temperature increased significantly. The precipitation in Central Asia showed an increasing trend as a whole(Wang et al.2008).The precipitation in the arid region of Central Asia is mainly affected by westerly circulation and North Atlantic Oscillation (NAO)(Aizen et al.2001).Northwest China, adjacent to the five Central Asian countries, is located between the arid and monsoon regions of Central Asia. It is a transitional region affected by westerly and monsoon, and its climate characteristics are complex(Huang et al.2012).At the beginning of the 21st century, some studies put forward the conclusion that there was a transition from the warm dry pattern to the warm wet pattern in northwest China, especially in Xinjiang ( Shi et al.2003 ). In recent years, the study of ' Climate Warming and Humidification ' in this area has attracted more and more attention from academia and governments at all levels ( Yao et al. 2022 ; kumar et al. 2015 ). Therefore, in the context of global warming, it is of practical significance to analyze the characteristics of dry‒wet changes in Xinjiang 's climate. In past research, there is no standard definition of 'dry' or 'wet'. The core of the characterization of dry‒wet climate change refers to a change in the surface water moisture availability/loss or in the balance of moisture income/expenditure (Wang et al., 2014). Under global warming, a single precipitation change analysis is not sufficient to reflect the true dry‒wet conditions of a region, especially in arid areas where precipitation is much less than evapotranspiration. Drought is generally considered to be the difference between the water supply in the sense of water availability and the atmospheric water demand through evapotranspiration (Thornthwaite, 1948), whereas the aridity index (or humidity index) takes into account both precipitation and evaporation and thereby provides a better picture of dry‒wet conditions than a single precipitation analysis can provide (Zhang et al., 2016). Many studies have also suggested that the aridity index can reflect the actual situation of the dry‒wet climate in an integrated manner (Yuan et al., 2017; Zhang et al., 2022). Yuan et al. (2017) used the annual aridity index to explore the temporal and spatial variabilities in the dry‒wet conditions of China's terrestrial climate from 1961 to 2015. The results showed that the regional differences in the trend and periodicity of dry‒wet climate changes in China were obvious, and the dry‒wet climate changes were highly correlated with precipitation in northern China. Li et al. (2015), Zhang et al. (2016), and Xu et al. (2017) analyzed the spatial and temporal distribution characteristics of the surface humidity index and extreme dry‒wet events in different regions of China and identified some differences in the trend of dry‒wet climate change among different regions. Ding et al. (2016) reported that Northwest China has experienced a shift from a warm-dry to warm-wet climate in recent decades, called the "warming and wetting" climate trend. The causes of localized wetting and precipitation increases in Northwest China are still uncertain, and there is still a great deal of uncertainty about whether the signs of warming and wetting will continue or expand in the future. There is also a lack of clear conclusions about the characteristics of this "warming and wetting" climate trend in the western part of Northwest China at varying temporal scales (Yao et al., 2022; Wang et al., 2020; Zhang et al., 2022). Donat et al. (2019) also pointed out that the aridity index (AI) can be used in arid regions, but the uncertainty in the results is high due to the poor observed coverage in most arid regions of the world. The previous research results provided above show that the impacts of global climate change on dry‒wet climate change in different regions are quite different and complex, and there are still many uncertainties regarding the regional-scale impact mechanisms. Therefore, for climate change adaptation and disaster risk prevention in Xinjiang, it is important to study the spatial and temporal evolution patterns of dry‒wet climate change and the corresponding impact factors in Xinjiang. As a typical continental arid climate area, Xinjiang has low precipitation, strong evaporation, a dry climate and a fragile ecological environment. Therefore, dry‒wet climate change significantly impacts economic and social development, ecological environment restoration and people's production and livelihoods in this area (Zhou et al., 2020; Zhao et al., 2019). At the beginning of the 21 st century, Shi et al. (2003) first proposed the conclusion that the climate in Northwest China, especially in Xinjiang, is "warming and wetting". In recent years, many studies have explored this "warming and wetting" climate trend in Xinjiang. However, the dry‒wet characteristics of Xinjiang differ due to different selection indicators, thus reflecting the complexity of climate humidification in arid areas. It is difficult to completely, objectively and clearly describe the dry‒wet evolution characteristics of Xinjiang considering only single climate factors such as precipitation or temperature (Yao et al., 2021). In this study, the aridity index, which takes into account both precipitation and potential evapotranspiration, is used to analyze the spatial and temporal evolution characteristics of dry‒wet climate change in Xinjiang over the past 60 years (1961-2020) to discover and summarize the evolution law of the dry‒wet climate in Xinjiang and better understand the regional climatic conditions and influencing factors. The rest of this paper is divided into the following sections. The second part is the data and methods, where we describe the observations used in this study and the research methods used in the analysis. In the third part, we present the results of the analysis. The fourth part contains the results and discussion. 2. Data and methods 2.1 Overview of the study area Xinjiang is located in the hinterland of the Eurasian continent along the northwestern border of China, far from the geographical location of the sea, and the unique topography of Xinjiang forms its continental, strongly temperate, arid climate. The climate in Xinjiang varies greatly from north to south. The average annual precipitation in Xinjiang is approximately 178 mm, and the average annual evaporation is 1000~4500 mm, making drought a fundamental attribute of Xinjiang’s climate. Figure 1 shows the Xinjiang region and the locations of the research stations (I: northern Xinjiang, II: Tianshan Mountains, and III: southern Xinjiang). 2.2 Data sources and methods 2.2.1 Meteorological data The National Information Center of the China Meteorological Administration has produced long-time-series ground homogenization observation data (Cao et al. 2016) that have been widely used in research in the climate change field because of their good data quality (Dong and Sun 2018; Dong et al., 2022; Li and Chen 2021; Zhang et al., 2022). From these data, 99 national meteorological stations in Xinjiang at which daily mean temperature, maximum temperature, minimum temperature, mean sunshine hours, mean relative humidity, mean wind speed and precipitation data were recorded with high data-series completeness from 1961 to 2020 were selected.Based on these basic data, the Aridity Index was calculated. 2.2.2 Calculation of AI and classification of the dry ‒ wet climate grade In this paper, AI was calculated as the ratio of the annual potential evapotranspiration to annual precipitation. The calculation formula is expressed as follows: where AI is the aridity index, P is the annual precipitation, and E 0 is annual potential evapotranspiration. In this paper, E 0 is calculated by the FAO56 Penman-Menteith method recommended in the“Meteorological Drought Grade standard” revised by Zhang et al.(Zhang et al.2017). The calculation method is as follows : Formula (2). where E 0 is the daily potential evapotranspiration (mm), G is the soil heat flux density (MJm -2 d -1 ), T is the daily mean temperature at the 2-m height (°C), u 2 is the wind speed at the 2-m height (ms -1 ), es is the mean saturated water vapor pressure (kPa), ea is the actual water vapor pressure (kPa), △ is the slope of the saturated water vapor pressure curve (kPa℃ -1 ), γ is the dry‒wet gauge constant (kPa°C -1 ), and R n is the surface net radiation (MJm -2 d -1 ). The surface net radiation (R n ) is calculated from the net surface shortwave radiation R ns and the net surface longwave radiation R nl . The net surface shortwave radiation Rns is calculated according to Formula (3) as follows: where R so is the extraterrestrial radiation (MJm -2 day -1 ), n is the actual sunshine hours (h), N is the maximum possible sunshine hours (h), the coefficient a s is the transmission coefficient of the extraterrestrial radiation reaching the ground on cloudy days, and a s +b s is the transmission rate of the extraterrestrial radiation reaching the ground on sunny days. The monthly a s and b s values are calculated according to the national standard of the 'dry‒wet' climate grade (MAO fei et al., 2017). By calculating the AI and referring to the classification method of the dry‒wet climate proposed by Zhang et al. (2021), the climate zone was classified into 6 grades, namely, a superhumid area, humid area, subhumid area, subarid area, arid area and extremely arid area. The specific classification criteria are shown in Table 1. Table 1. Grades of dry‒wet climate division Grade 1 2 3 4 5 6 Climate zone Superhumid Humid Subhumid Subarid Arid Extremely arid AI <0.5 0.5~1.0 1.0~1.5 1.5~3.5 3.5~20.0 ≥20.0 2.2.3 Other research methods The nonparametric Theil-Sen median estimation and Mann-Kendall test were used to estimate the linear trend. Sen's slope estimation is a nonparametric test method proposed and developed by Sen in 1968 (Sen, 1968). Zhang et al. (2000) estimated the trend slope of N to the data in n samples (Jiang et al., 2015). When the calculation result is greater than 0, the time series to be analyzed shows an upward trend. When the calculation result is less than 0, the time series to be analyzed shows a downward trend. The Mann-Kendall test was used to detect the mutation of the annual average AI value in Xinjiang. The Mann-Kendall test is a nonparametric test method. Compared to parametric test methods, the Mann-Kendall test does not require the sample to follow a certain distribution and is not affected by the presence of a few outliers. This test is thus more suitable for sequential variables (Fensholt, 2012 and Yue, 2002). In this paper, Morlet wavelet analysis is used to study the periodicity of the dry‒wet changes.The wavelet transform method has the characteristics of multiresolution analysis and has the ability to characterize the local characteristics of a signal in both the temporal and frequency domains. By decomposing a time series into the time-frequency domain, the significant fluctuation pattern of the time series can be obtained; that is, the periodic change dynamics and the time pattern of the periodic change dynamics can be determined (Morlet, 1982 and Torrence et al., 1998). This paper also uses the natural orthogonal function (EOF) decomposition method is used to study the distribution characteristics of dry‒wet spatial anomalies in Xinjiang.Empirical orthogonal function (EOF) analysis, also known as eigenvector analysis, is a method used to analyze the structural characteristics of matrix data and extract the main data features. Also called principal component analysis, it is the most commonly used linear dimension reduction method. The goal of this analysis method is to map high-dimensional data into a low-dimensional space through some linear projection while expecting to maximize the amount of information (variance) of the data in the projected dimension to allow fewer data dimensions to be used while retaining as many original data points as possible. The EOF analysis method can decompose a temporally varying variable field into a spatial function component that does not change temporally and a temporal function part that depends only on time and does not change spatially (Pang et al., 2014; Cai et al., 2022). 3. Results 3.1 Temporal characteristics of the dry‒wet climate in Xinjiang in the last 60 years From 1961 to 2020, the annual AI of Xinjiang showed a significant downward trend, decreasing by 2.1 per decade (passing the 0.05 significance test); this result reflects the fact that the climate in Xinjiang has become more humid over the past 60 years. The aridity index showed a turning point in the late 1980s; the values were high in the early stage and low in the late stage, indicating that the climate in Xinjiang was relatively dry before the late 1980s and relatively wet after this period. The year 2020 was the wettest year in Xinjiang in the past 60 years, with an annual aridity index of 4.71, while 1968 was the driest year in Xinjiang in the past 60 years, with an annual AI of 49.67. In terms of the annual trend, the AI showed a decreasing trend year-by-year. The AIs of each decade were 26.44 in the 1960s, 22.76 in the 1970s, 18.63 in the 1980s, 16.03 in the 1990s, 15.30 in the 2000s and 13.59 in the 2010s(Fig. 2). Xinjiang has a vast territory, and the climate difference between southern and northern Xinjiang is large. Therefore, the Xinjiang Geographical Division divides Xinjiang into northern Xinjiang, the Tianshan Mountains and southern Xinjiang. The annual AI trends in northern Xinjiang, Tianshan Mountains, and southern Xinjiang are consistent with those in Xinjiang overall, showing significant downward trends, that is, consistent wetting trends, with decline rates of 0.42, 0.22 and 4.58/10 a, respectively. The annual AI values of northern and southern Xinjiang decreased each year, while that of the Tianshan Mountains showed a decreasing trend with interdecadal fluctuations; in the three regions, the AIs decreased by 34%, 33% and 56%, respectively, in the 2010s compared to the 1960s. Among the three subregions, the southern Xinjiang region is the most humid, followed by northern Xinjiang and then the Tianshan Mountains. The wettest year in northern Xinjiang was 2016, with an annual aridity index of 2.64, and the driest year was 1962, with an annual aridity index of 8.84. The wettest year in the Tianshan Mountains was 1998, with an annual aridity index of 1.63, and the driest year was 1985, with an annual aridity index of 6.17. The wettest year in southern Xinjiang was 2020, when the annual aridity index was 2.86; the driest year was 1968, when the annual aridity index was 98.41. From the perspective of the spatial variation in the annual drought index corresponding to the Sen trend results in Xinjiang, the annual aridity index in Xinjiang has shown a downward trend over the past 60 years; that is, it has shown a wetting trend, especially in the southern Basin and eastern Xinjiang. This wetting trend is most obvious, and the aridity index decreases by more than 5 per decade on average, among which those in the Bayingolin Mongol Autonomous Prefecture and some areas of Turpan City decreased by 8-16. The aridity index values in most areas of northern Xinjiang decreased by 0.05~5 per decade. This conclusion is similar to the previous conclusions obtained from analyses of drought indices such as the meteorological drought composite index (MCI), Palmer drought severity index (PDSI) and precipitation index (Wu et al., 2022; Guo et al., 2022). The significance test showed that 83% of the stations in Xinjiang recorded significant downward trends in the annual AI passing the 0.05 significance test. That is, most of Xinjiang became significantly wetter. Only the southern Ili Valley, Tacheng, Turpan, Hotan and southern Bayingolin Mongol Autonomous Prefecture did not pass the 0.05 reliability test (Figure 3). 3.2 Spatial distribution characteristics of AI climate zones in Xinjiang From the distributions of the average AI and average precipitation from 1961 to 2020, the climate zone divisions obtained from the aridity index results in Xinjiang are consistent with the regional boundaries divided by precipitation (Figure 4). Most of northern Xinjiang and the western part of southern Xinjiang receive precipitation totals between 100 and 200 mm, and the average annual aridity index in these regions is less than 20. Most of these areas are arid and subarid. Among them, the average annual precipitation totals in some areas of the Ili River Valley and the southern mountainous areas of Urumqi, Tianchi and other Tianshan Mountains are greater than 300 mm, and the average annual aridity index is less than 3.5, corresponding to subarid and subhumid areas. The average annual precipitation totals in the southern Xinjiang basin and the eastern Xinjiang region are mostly less than 100 mm and, in some areas, even less than 50 mm, indicating perennially arid areas. The average annual aridity index values here are greater than 20, suggesting extremely arid areas. 3.3 Interdecadal variability characteristics of dry ‒ wet climate zones in Xinjiang Taking the mean value of the aridity index from 1961 to 1990 as the base period, the AI anomalies in the Tacheng area, northern Altay area, Tianshan Mountain area, western southern Xinjiang, Bayingolin Mongol Autonomous Prefecture, and eastern Xinjiang region in the 1960s were positive, indicating drier conditions than those in the base period, especially in the southern part of Bayingolin Mongol Autonomous Prefecture and the local area of Hami City. In the 1970s, most of Xinjiang still had positive anomalies, with drier conditions than the base period. Compared to the 1960s, the driest area began to move to the Hotan area in the southern Tarim Basin, and most of the Tianshan Mountains and eastern Xinjiang had negative anomalies, with wetter conditions than the base period. Since the 1980s, most areas of Xinjiang have become wetter, with significantly increased wet conditions compared to the previous two years. That is, the western part of southern and northern Xinjiang and the eastern part of the Tarim Basin had negative anomalies, while the eastern part of Changji Prefecture, the western part of Hotan and most of Kashgar were dry areas. In the 1990s, the aridity index values in most areas of Xinjiang exhibited negative anomalies, with significantly wetter conditions than those in the base period, especially in the southern Xinjiang Basin and most parts of eastern Xinjiang. In the 2000s and 2010s, most of northern Xinjiang, the Tianshan Mountains, Hami City in eastern Xinjiang, Kashgar, Kizilsu Kirghiz Autonomous Prefecture and Hotan in western southern Xinjiang were wet, especially in southern Xinjiang and eastern Xinjiang. With further wetting, only the eastern part of Changji Prefecture was a dry area. In general, the dry‒wet changes in Xinjiang reversed between the 1980s and the 1990s. Before this period, most of Xinjiang showed a drying trend. Since the 1980s, Xinjiang has tended to be wet, and this trend is more pronounced in the southern Xinjiang region(Figure 5). The AI sequence of the Xinjiang region was calculated according to the classification standard of 6 dry‒wet grades adopted in this paper. There are 5 kinds of climate zones in Xinjiang, including the extremely arid region, arid region, subarid region, subhumid region and humid region. From the average value during the 1961-2020 period, the station ratio of the arid climate zone (including extremely arid, arid and subarid areas) in Xinjiang was 93%, while that of the humid climate zone (including subhumid and humid areas) was only 7% and was distributed mainly in the Tianshan Mountains. In the 1961-2020 period, the total station ratio of extremely arid and arid areas decreased annually; overall, the number of stations in the extremely arid area decreased from 38% in the 1960s to 14% in the 2010s. The ratio of stations in arid areas fluctuated, with the lowest observed in the 1960s (45%) and the highest observed in the 2000s (52%). The total station ratio of subarid, humid and semihumid areas increased from 12% in the 1960s to 32% in the 2010s, and the station ratio of humid and subhumid areas increased from 4% in the 1960s to 7% in the 2010s. The subarid, humid and semihumid areas showed significant increasing trends, the extremely arid area showed a significant decreasing trend, and the arid area showed fluctuating changes. It can also be seen that the Xinjiang region as a whole showed a wetting trend (Figure 6). By analyzing the station ratios of different climate zones in each subregion, it can be seen that the climate zones included in northern Xinjiang are mainly arid and semiarid regions. The station ratio of arid climate zones identified in each decade decreased annually, while that of subarid regions increased annually. The station ratio of arid regions decreased from 32% in the 1960s to 14% in the 2010s, while that of subarid regions increased from 8% in the 1960s to 26% in the 2010s. The climate zones in the Tianshan Mountains mainly include arid, subarid, humid and subhumid areas. From the perspective of decadal changes, the humid and subhumid areas, subarid areas in the Tianshan Mountains showed increasing trends, increasing by 4% and 1%, respectively, from the 1960s to the 2010s, while the arid area showed a decreasing trend, decreasing by 4% from the 1960s to the 2010s. The southern Xinjiang region contains only extremely arid and arid climate zones. Since the 1960s, the extremely arid climate zone area in this region has shown a significant decreasing trend, while the arid climate zone area has shown a significant increasing trend. From the 1960s to the 2010s, the station ratio in the arid zone increased by 2 times (16%), while that in the extremely arid climate zone decreased by 70% (24%). In general, from the perspective of the decadal changes from 1960-2020, the station ratios of the humid climate zones in the three subregions of Xinjiang increased, while the proportion of the arid climate zone decreased. It can also be seen from Table 1 that each subregion showed a significant wetting trend, especially since the 21 st century. Table 1 Ratios of stations in different climate zones of Xinjiang in each decadal period (%) 1960s 1970s 1980s 1990s 2000s 2010s Northern Xinjiang Subarid 8 9 14 17 22 26 Arid 32 31 26 23 18 14 Tianshan Mountains Humid and subhumid 4 5 6 6 8 8 Subarid 4 3 4 6 5 5 Arid 5 5 4 2 2 1 Southern Xinjiang Arid 8 14 22 24 32 32 Extremely arid 37 31 23 21 13 13 3.4 Analysis of the dry ‒ wet climate cycle and mutation detection in Xinjiang From the AI wavelet analysis results in Xinjiang shown in Fig. 7(a), the original AI curve changed in a disorganized way, and no obvious periodicity can be seen. Through Morlet wavelet analysis, it can be clearly seen from Figs. 7 (b) and (c) that the periodicity of the dry‒wet changes in Xinjiang is significant, mainly with 2.5-year and 6-year periodic changes, and the confidence of both periods exceeds 95%. According to the abrupt AI change detection results obtained in Xinjiang over the past 60 years in Figure 8, the UB and UF curves intersected in 1986, and the UF curve passed the critical line of the 0.05 level in 1992; that is, the abrupt change in the wetting trend in Xinjiang reached significance in this year, and then the AI basically maintained a decreasing trend only in 1997, 2001, 2019 while again increasing in the other individual years. Thus, Xinjiang has experienced a continuous wetting process since 1986. 3.5 Analysis of the spatial and temporal characteristics of the dry ‒ wet climate in Xinjiang To further understand the spatial distribution characteristics of the dry‒wet climate in Xinjiang, the annual AI values at 99 stations were expanded using the empirical orthogonal function (EOF). The results showed that the first three eigenvectors were the main components, with variance contribution rates of 40.62%, 26.15% and 10.16%, respectively, and a cumulative contribution variance rate of 76.93%. The variance contribution rate of each feature vector after the third feature vector was small and thus could be ignored. The first eigenvector (Fig. 9a) values of the EOF decomposition were negative in the whole Xinjiang region, representing the consistent change in the dry‒wet climate in Xinjiang; that is, the climate in Xinjiang was generally either dry or wet. The intensity of this change varied among different regions. The eastern and southern Xinjiang basins were the main large-value areas and the main control areas of the first mode. The time coefficient PC1 (Fig. 9b) corresponding to the first mode shows a clear, abrupt change over time, with the time coefficients for the 1961-1986 period being predominantly negative, indicating that the climate in Xinjiang was generally dry during this period, with the lowest coefficient observed in 1985, corresponding to the driest period in this region. Since 1987, the time coefficient has been dominated by a positive orientation, indicating that the whole climate in Xinjiang has been wet after this period. The second eigenvector (Fig. 9c) differed significantly from the first eigenvector. The eigenvector values of the northern and southern Xinjiang basins were negative, while those of the Tianshan Mountains were positive, reflecting the opposite spatial distribution characteristics of the dry‒wet climate in northern and southern Xinjiang compared to those in the Tianshan Mountains. The negative large-value area is located in the southeastern Tarim Basin, and the positive large-value area is located in Turpan. The time coefficient PC2 (Fig. 9d) corresponding to the second mode shows the same trend as the large PC1 value; that is, before 1987, the values were mainly negative, and since 1987, they were mainly positive. The third eigenvector (Fig. 9e) was positive in most parts of Xinjiang and negative only in southern Bazhou, Turpan and southern Hami, thus representing the consistent change in dry and wet conditions in most parts of Xinjiang, while Turpan, southern Hami and southern Bazhou showed the opposite change. The positive high-value areas of the modal eigenvector were located in Hotan and Kashgar. The time coefficient PC3 (Fig. 9f) corresponding to the third mode was reflected spatially in the alternation of heavy and light drought events in southern Bazhou, Turpan and Hami with most of western Xinjiang. The time coefficient was the smallest in 1974, thus accurately reflecting the severe drought events that occurred at this time in most of Xinjiang. 4. Conclusion and discussion Based on the daily data of 99 meteorological stations in Xinjiang in the past 60 years (1961-2020), the potential evapotranspiration was calculated by using the modified FAO56 Penman-Monteith model. On this basis, the aridity index(AI) was calculated, and then the spatial and temporal evolution characteristics of dry and wet climate in Xinjiang were analyzed in detail by using interannual AI, which was helpful to better understand the regional and interannual changes of climate change in this area. The main conclusions are as follows :The main conclusions are as follows. From the average climatological AI state from 1961 to 2020, northern and southern Xinjiang were found to be arid and subarid regions, respectively. The Tianshan Mountains, including parts of the Ili River Valley and the southern mountainous areas of Urumqi, were found to be subhumid areas; and the rest of Xinjiang, especially the eastern and southern Xinjiang basins, was found to contain extremely arid areas. In the past 60 years, the annual AI of Xinjiang showed a significant downward trend, and the climate tendency rate was 2.4/10a; that is, the climate of Xinjiang has become wetter over the past 60 years. From the perspective of each subregion, the changes in northern Xinjiang, the Tianshan Mountains and southern Xinjiang all showed consistent wetting trends, especially in southern Xinjiang; in these subregions, the annual AI values decreased by 0.45, 0.23 and 5.23/10 a, respectively. According to the analysis of the station ratios of different climate zones from the 1960s-2010s, the total station ratio of arid and extremely arid areas in Xinjiang showed a decreasing trend, while the total station ratios of subarid areas and humid and semihumid areas showed increasing trends, especially since the 21 st century. The wavelet analysis and abrupt change detection showed that the dry‒wet changes were most pronounced in a 6-year cycle, and Xinjiang has been in a continuous wetting process since 1986, with a significant abrupt change occurring in 1992. The first EOF decomposition mode was consistent in Xinjiang; that is, the climate in Xinjiang was generally either dry or wet. The intensity of this change differed among different regions. The eastern and southern Xinjiang basins were the main large-value areas, that is, the main control area of the first mode. The corresponding time coefficient also accurately reflects that the year of dry‒wet mutation in Xinjiang was 1986. The second mode reflected the opposite spatial distribution characteristics of dry‒wet climate change in southern and northern Xinjiang compared to the Tianshan Mountains. The third mode reflected that the dry‒wet changes were consistent among most areas of Xinjiang, though the eastern part of Xinjiang and the southern part of Bayingolin Mongol Autonomous Prefecture showed the opposite characteristics. From the interdecadal spatial evolution characteristics of the dry‒wet climate in Xinjiang, it can be seen that the dry‒wet climate trend reversed in the 1980s and 1990s. Before this period, Xinjiang's climate was mainly dry. After this period, a wetting trend was observed compared to the base period. The dry area in the southern Xinjiang basin expanded slightly in the 2000s and shrank again in the 2010s, while the dry area expanded in the 2010s in the eastern Xinjiang. The AI of each subregion in Xinjiang showed a decreasing trend with different degrees from 1961 to 2020; that is, the overall climate showed a 'wetting' trend, consistent with most previous studies on dry‒wet changes in Xinjiang (Jiang et al., 2009; Zhang et al., 2010). Yao Junqiang et al. (2021) also pointed out that since the middle and late 1980s, the temperatures in Xinjiang have increased, and the precipitation amounts have also increased, showing the characteristics of "warming and wetting." This study also pointed out that since 1997, the climate in Xinjiang has shown a strong signal change from "warm and humid" to "warm and dry." After this year, the AI remained at a high level but still showed a deceasing trend; thus, the climate of Xinjiang was still becoming generally wetter. It is worth noting that although the area is affected by both precipitation and potential evapotranspiration, Xinjiang, as an important component of the arid region of Northwest China, also has a strong signal of rising temperatures and increasing precipitation. However, due to its geographical location far from the ocean and distantly inland, such strong warming is accompanied by vast potential evaporation, and the absolute value of the precipitation increase is thus not high (the annual precipitation increase rate in Xinjiang is only 10.14 mm/decade). Therefore, this observed trend is not expected to change the nature of Xinjiang as an arid region in the short term and is far from sufficient to produce qualitative changes in the regional dry‒wet conditions. Simulations of the future climate change trends in Xinjiang and Northwest China show an overall wetting trend (Zhang et al., 2022; Feng et al., 2019). Feng et al. (2019) used the greenhouse gas scenario emission concentration provided by IPCC AR5 to simulate and test climate change in the northwest region from 1951 to 2015 and predict the climate change trend in the northwest region over the next 10 years. Their results show that by 2030, the annual average temperature in the northwest region is expected to show an increasing trend, with more complex precipitation change and an overall increase in aridity index. Zhang et al. (2022) calculated the regional aridity index (AI) of China from 2020 to 2099 by using projected model data and analyzed the dry‒wet climate change trends of China from 2020 to the end of this century under the RCP4.5 and RCP8.5 scenarios. They concluded that under both scenarios, the wetting trend in the western part of China will be more pronounced with larger range under the RCP8.5 scenario than under the RCP4.5 scenario. Based on 21 available CMIP5 models, Wang et al. (2020) showed that in the case of global temperature increases of 2 °C and 4 °C, the dry‒wet changes indicated by the AI showed that the drought area will increase most in semiarid areas, followed by in arid areas. Therefore, if the variation characteristics of the aridity index in different regions and times in Xinjiang continue in the future, the flood and drought events in northern and southern Xinjiang will also change greatly, thus critically impacting agriculture and society. In the future, the projected changes in the AI in Xinjiang will be analyzed. Declarations Funding This study was funded by the Xinjiang Natural Science Foundation (2022D01B181) and LCPS Youth Fund(2022). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Xiulan Wu: Investigation,Methodology, Writing-original draft, Writing-review & editing. Cunjie Zhang: Methodology, Data curation. Siyan Dong: Supervision,Conceptualization,Writing- review & editing. Jiahui Hu: Visualization,Software. Xinyi Tong: Project administration,Resources. Xiannian Zheng: Validation,Software. References Chen F H, Huang W, Jin L Y, et al. 2011.Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming. Sci China Earth Sci, 54,1812–1821, doi: 10.1007/s11430-011-4333-8. Donat M G, Angélil O, Ukkola A M. 2019.Intensification of precipitation extremes in the world’s humid and water-limited regions. Environmental Research Letters. 14,065003. Ma,D.,Yin,Y.,Wu S.,et al.,2019.Sensitivity of arid/humid patterns in China to future climate change under high emission scenario .J.Geogr Sci.29,29-48(Chinese). Wang J,Chen F,Jin L,et al.2008.The Response to Two Global Warming Periods in the 20th Century over the Arid Central Asia.J. Glaciolo.Geocryolo.2,224-233(Chinese). Aizen, E. M., Aizen, V. B., Melack, J. M., et al. 2001. 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Yao J.,Mao W.,Chen J,et al.2021.Signal and impact of wet-to-dry shift over Xinjiang, China.Acta Geographica Sinica.76,57-72. Yuan Q. , Wu S., Zhao D., et al .2017. Spatio - temporal variation of the wet-dry conditions from 196l to 2015 in China . Sci. China: Earth Sci.60,2041-2050, doi :10.1007/s11430-017-9097-1(Chinese). Yue S,Pilon P,Cavadias G.2002.Power of the Mann-Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series.J. Hydrology.259, 254-271． Zhang C, Ren Y, Cao L, et al. 2022. Characteristics of dry-wet climate change in China during the past 60 years and its trends projection. Atmosphere, 13, 275. Zhang C., Liao Y., Duan J.,et al.2016.The progress in dry-wet climate divisional research in China . Clim. Change Res.12, 261-267(Chinese). Zhang J.,Shi Y..2002.Study on climate change and short-term climate prediction in Xinjiang.Beijing: Meteorological Publishing House. (Chinese). Zhang Q,Zhang C.,Bai H.,etal.2010.New development of climate change in Northwest China and its impacton arid environment.J. Arid Meteorology,28,1-7(chinese). Zhang X, Vincent L ., Hogg W., et al. 2000. Temperature and precipitation trends in Canada during the 20th century. Atmos.-Ocean. 38, 395-429. Zhang Y,An C.,Liu L., et al. 2022.High-elevation landforms are experiencing more remarkable wetting trends in arid Central Asia. Adv. Clim. Change Res.13, 489-495. Zhang, Hongli, et al. 2016.Aridity over a semiarid zone in northern China and responses to the East Asian summer monsoon. J. Geophys. Res. Atmos. 121,13901-13918. Zhao H., Xue B..2019. What were the changing trends of the seasonal and annual aridity indexes in northwestern China during 1961–2015?Atmos Res .222, 54-162. Zhou J, Jiang T,Wang Y.,et al.2020.Spatiotemporal variations of aridity index over the Belt and Road region under the 1.5℃ and 2.0℃ warming scenarios. J. Geophys. Sci.30,37-52. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Aug, 2023 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Major revision 13 May, 2023 Reviews received at journal 10 May, 2023 Reviewers agreed at journal 06 May, 2023 Reviewers invited by journal 06 May, 2023 Submission checks completed at journal 14 Mar, 2023 Editor assigned by journal 14 Mar, 2023 First submitted to journal 13 Mar, 2023 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-2689317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":183287021,"identity":"dfd55fea-6360-4f8e-8c7e-abf1d730c834","order_by":0,"name":"Xiulan Wu","email":"","orcid":"","institution":"Xinjiang Climate Center, China Meteorological Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiulan","middleName":"","lastName":"Wu","suffix":""},{"id":183287023,"identity":"60ffa440-a077-4422-83df-1046a3d2e0b6","order_by":1,"name":"Cunjie Zhang","email":"","orcid":"","institution":"National Climate Center, China Meteorological Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cunjie","middleName":"","lastName":"Zhang","suffix":""},{"id":183287025,"identity":"1c7c7786-b47e-40a0-b4e1-c45cd7f66ab9","order_by":2,"name":"Siyan Dong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDCCA2DShnQtaaRrOUyCDr7jvYdf/mw7L6/bfoD5xcc2BnlzQlokz5xLs+Ztu2247UwCm+XMNgbDnQ0EtBjcyDEzZmy7nWB2IIHNmOcMQ4LBASK0GP5sO5dgdv4B8VqMH/C2HUgwu5HA/JingggtkmfOmDHznEs23HbjYRvjjAoJww2EtPAd7zH++KPMTt7sfPLhDx8MbOQJ2gIEbBKMbCCasU2CgUGCsHogYP7A8AfGGAWjYBSMglGABQAAdSpFG142qogAAAAASUVORK5CYII=","orcid":"","institution":"China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Siyan","middleName":"","lastName":"Dong","suffix":""},{"id":183287026,"identity":"f14cc590-4023-4701-a901-b7a23eaa6e50","order_by":3,"name":"Jiahui Hu","email":"","orcid":"","institution":"Xinjiang Climate Center, China Meteorological Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Hu","suffix":""},{"id":183287028,"identity":"9e0633e6-1302-4a70-89e8-abdf304d1bb2","order_by":4,"name":"Xinyi Tong","email":"","orcid":"","institution":"Xinjiang Climate Center, China Meteorological Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Tong","suffix":""},{"id":183287029,"identity":"505e6499-3708-4729-ba99-1c4f8dff7e23","order_by":5,"name":"Xiannian Zheng","email":"","orcid":"","institution":"Xinjiang Climate Center, China Meteorological Administration","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiannian","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2023-03-14 01:14:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2689317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2689317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-023-11070-3","type":"published","date":"2023-08-09T21:57:32+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":34423087,"identity":"7c96b3c2-e7b4-4724-b1be-c015aed11999","added_by":"auto","created_at":"2023-03-17 14:51:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":353858,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the study area (I: northern Xinjiang, II: Tianshan Mountains, and III: southern Xinjiang)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/f9fac0b0c21e8c874bfc2ce9.png"},{"id":34421457,"identity":"64a01e14-d93a-4105-8b41-420992a0c457","added_by":"auto","created_at":"2023-03-17 14:43:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34675,"visible":true,"origin":"","legend":"\u003cp\u003eSen trend of the annual average AI in Xinjiang\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/1f04e749490d9ffc6ee2bd61.png"},{"id":34421458,"identity":"090bea1a-8e3b-4ec3-9350-3b6d462364a4","added_by":"auto","created_at":"2023-03-17 14:43:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260098,"visible":true,"origin":"","legend":"\u003cp\u003eSen linear trend results of the annual AI (/10a)in Xinjiang from 1961 to 2020\u003c/p\u003e\n\u003cp\u003e(Solid circles pass the 0.05 reliability test; hollow circles fail the 0.05 reliability test)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/068be10c601381f4d2a6315d.png"},{"id":34421459,"identity":"97c614a1-a2a3-46c9-a34a-afc2dc138764","added_by":"auto","created_at":"2023-03-17 14:43:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":196476,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution diagram of the average AI (color spot) and annual average precipitation (dotted line) in Xinjiang from 1961 to 2020\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/cf7e3b85744085137d46c881.png"},{"id":34421461,"identity":"5ed84b30-46c0-4f0e-8684-b9aa4e0d7ddd","added_by":"auto","created_at":"2023-03-17 14:43:42","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1592455,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of AI anomaly values in Xinjiang from the 1960s to the 2010s (a~f)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/216d08db7e48be99903e42d5.png"},{"id":34421465,"identity":"353763cb-800f-4bad-b843-181ea6683df8","added_by":"auto","created_at":"2023-03-17 14:43:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10110,"visible":true,"origin":"","legend":"\u003cp\u003eInterdecadal variations in station ratios in different climate zones\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/1c485d82c7d310a844c3107c.png"},{"id":34421462,"identity":"3b490d28-d447-44b4-93dd-7d210080975a","added_by":"auto","created_at":"2023-03-17 14:43:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":169173,"visible":true,"origin":"","legend":"\u003cp\u003eWavelet analysis results of the annual AI:\u003c/p\u003e\n\u003cp\u003e(a) AI source data series, (b) Morlet wavelet analysis power spectrum, and\u003c/p\u003e\n\u003cp\u003e(c) significant periodic spectrum (red dashed line is the 95% confidence interval)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/39b2b80ab57f3f5598f4066d.png"},{"id":34423088,"identity":"ed9c26cc-a4ce-4a4c-bb90-cb658ac22249","added_by":"auto","created_at":"2023-03-17 14:51:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":165537,"visible":true,"origin":"","legend":"\u003cp\u003eXinjiang annual AI mutation detection results (0.05 significance level)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/c863aa329a428ff7779aaa14.png"},{"id":34421464,"identity":"279e0679-c8ec-40d5-90b8-6dc3126c925e","added_by":"auto","created_at":"2023-03-17 14:43:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":572977,"visible":true,"origin":"","legend":"\u003cp\u003eFirst three spatial modes (a, c, and e) and their corresponding time series (b, d, and f) of the EOF expansion results of the annual AI values in Xinjiang from 1961 to 2020.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/d01edaf86d1e372639db4b6e.png"},{"id":44736639,"identity":"39404dcb-5e6e-4b98-a8c0-68e1f2dbbd59","added_by":"auto","created_at":"2023-10-16 22:31:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3069558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2689317/v1/65088296-2c2d-4cad-bacc-f81a728ab310.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal dynamics of the aridity index in Xinjiang over the past 60 years","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the past few decades, climate change has had increasingly significant impacts on the global and regional water cycles and the distribution of dry‒wet climate zones.The arid region of central Asia is the largest non-zonal arid region in the world. Its ecological environment is fragile and sensitive to global climate change(Chen et al.2011;Donat et al.2019;Ma et al.2019).The arid region of Central Asia responded significantly to the second global warming in the 20 th century, and the temperature increased significantly. The precipitation in Central Asia showed an increasing trend as a whole(Wang et al.2008).The precipitation in the arid region of Central Asia is mainly affected by westerly circulation and North Atlantic Oscillation (NAO)(Aizen et al.2001).Northwest China, adjacent to the five Central Asian countries, is located between the arid and monsoon regions of Central Asia. It is a transitional region affected by westerly and monsoon, and its climate characteristics are complex(Huang et al.2012).At the beginning of the 21st century, some studies put forward the conclusion that there was a transition from the warm dry pattern to the warm wet pattern in northwest China, especially in Xinjiang ( Shi et al.2003 ). In recent years, the study of \u0026apos; Climate Warming and Humidification \u0026apos; in this area has attracted more and more attention from academia and governments at all levels ( Yao et al. 2022 ; kumar et al. 2015 ). Therefore, in the context of global warming, it is of practical significance to analyze the characteristics of dry‒wet changes in Xinjiang \u0026apos;s climate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn past research, there is no standard definition of \u0026apos;dry\u0026apos; or \u0026apos;wet\u0026apos;. The core of the characterization of dry‒wet climate change refers to a change in the surface water moisture availability/loss or in the balance of moisture income/expenditure (Wang et al., 2014). Under global warming, a single precipitation change analysis is not sufficient to reflect the true dry‒wet conditions of a region, especially in arid areas where precipitation is much less than evapotranspiration. Drought is generally considered to be the difference between the water supply in the sense of water availability and the atmospheric water demand through evapotranspiration (Thornthwaite, 1948), whereas the aridity index (or humidity index) takes into account both precipitation and evaporation and thereby provides a better picture of dry‒wet conditions than a single precipitation analysis can provide (Zhang et al., 2016). Many studies have also suggested that the aridity index can reflect the actual situation of the dry‒wet climate in an integrated manner (Yuan et al., 2017; Zhang et al., 2022). Yuan et al. (2017) used the annual aridity index to explore the temporal and spatial variabilities in the dry‒wet conditions of China\u0026apos;s terrestrial climate from 1961 to 2015. The results showed that the regional differences in the trend and periodicity of dry‒wet climate changes in China were obvious, and the dry‒wet climate changes were highly correlated with precipitation in northern China. Li et al. (2015), Zhang et al. (2016), and Xu et al. (2017) analyzed the spatial and temporal distribution characteristics of the surface humidity index and extreme dry‒wet events in different regions of China and identified some differences in the trend of dry‒wet climate change among different regions. Ding et al. (2016) reported that Northwest China has experienced a shift from a warm-dry to warm-wet climate in recent decades, called the \u0026quot;warming and wetting\u0026quot; climate trend. The causes of localized wetting and precipitation increases in Northwest China are still uncertain, and there is still a great deal of uncertainty about whether the signs of warming and wetting will continue or expand in the future. There is also a lack of clear conclusions about the characteristics of this \u0026quot;warming and wetting\u0026quot; climate trend in the western part of Northwest China at varying temporal scales (Yao et al., 2022; Wang et al., 2020; Zhang et al., 2022). Donat et al. (2019) also pointed out that the aridity index (AI) can be used in arid regions, but the uncertainty in the results is high due to the poor observed coverage in most arid regions of the world. The previous research results provided above show that the impacts of global climate change on dry‒wet climate change in different regions are quite different and complex, and there are still many uncertainties regarding the regional-scale impact mechanisms. Therefore, for climate change adaptation and disaster risk prevention in Xinjiang, it is important to study the spatial and temporal evolution patterns of dry‒wet climate change and the corresponding impact factors in Xinjiang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a typical continental arid climate area, Xinjiang has low precipitation, strong evaporation, a dry climate and a fragile ecological environment. Therefore, dry‒wet climate change significantly impacts economic and social development, ecological environment restoration and people\u0026apos;s production and livelihoods in this area (Zhou et al., 2020; Zhao et al., 2019). At the beginning of the 21\u003csup\u003est\u003c/sup\u003e century, Shi et al. (2003) first proposed the conclusion that the climate in Northwest China, especially in Xinjiang, is \u0026quot;warming and wetting\u0026quot;. In recent years, many studies have explored this \u0026quot;warming and wetting\u0026quot; climate trend in Xinjiang. However, the dry‒wet characteristics of Xinjiang differ due to different selection indicators, thus reflecting the complexity of climate humidification in arid areas. It is difficult to completely, objectively and clearly describe the dry‒wet evolution characteristics of Xinjiang considering only single climate factors such as precipitation or temperature (Yao et al., 2021). In this study, the aridity index, which takes into account both precipitation and potential evapotranspiration, is used to analyze the spatial and temporal evolution characteristics of dry‒wet climate change in Xinjiang over the past 60 years (1961-2020) to discover and summarize the evolution law of the dry‒wet climate in Xinjiang and better understand the regional climatic conditions and influencing factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rest of this paper is divided into the following sections. The second part is the data and methods, where we describe the observations used in this study and the research methods used in the analysis. In the third part, we present the results of the analysis. The fourth part contains the results and discussion.\u003c/p\u003e"},{"header":"2. Data and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Overview of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXinjiang is located in the hinterland of the Eurasian continent along the northwestern border of China, far from the geographical location of the sea, and the unique topography of Xinjiang forms its continental, strongly temperate, arid climate. The climate in Xinjiang varies greatly from north to south. The average annual precipitation in Xinjiang is approximately 178 mm, and the average annual evaporation is 1000~4500 mm, making drought a fundamental attribute of Xinjiang\u0026rsquo;s climate. Figure 1 shows the Xinjiang region and the locations of the research stations (I: northern Xinjiang, II: Tianshan Mountains, and III: southern Xinjiang).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Data sources and methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Meteorological data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Information Center of the China Meteorological Administration has produced long-time-series ground homogenization observation data (Cao et al. 2016) that have been widely used in research in the climate change field because of their good data quality (Dong and Sun 2018; Dong et al., 2022; Li and Chen 2021; Zhang et al., 2022). From these data, 99 national meteorological stations in Xinjiang at which daily mean temperature, maximum temperature, minimum temperature, mean sunshine hours, mean relative humidity, mean wind speed and precipitation data were recorded with high data-series completeness from 1961 to 2020 were selected.Based on these basic data, the Aridity Index was calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Calculation of AI and classification of the dry\u003c/strong\u003e\u003cstrong\u003e‒\u003c/strong\u003e\u003cstrong\u003ewet climate grade\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this paper, AI was calculated as the ratio of the annual potential evapotranspiration to annual precipitation. The calculation formula is expressed as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"515\" height=\"85\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere AI is the aridity index, P is the annual precipitation, and E\u003csub\u003e0\u003c/sub\u003e is annual potential evapotranspiration. In this paper, E\u003csub\u003e0\u003c/sub\u003e is calculated by the FAO56 Penman-Menteith method recommended in the\u0026ldquo;Meteorological Drought Grade standard\u0026rdquo; revised by Zhang et al.(Zhang et al.2017). The calculation method is as follows : Formula (2).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"761\" height=\"130\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere E\u003csub\u003e0\u003c/sub\u003e is the daily potential evapotranspiration (mm), G is the soil heat flux density (MJm\u003csup\u003e-2\u003c/sup\u003ed\u003csup\u003e-1\u003c/sup\u003e), T is the daily mean temperature at the 2-m height (\u0026deg;C), u\u003csub\u003e2\u003c/sub\u003e is the wind speed at the 2-m height (ms\u003csup\u003e-1\u003c/sup\u003e), es is the mean saturated water vapor pressure (kPa), ea is the actual water vapor pressure (kPa), △ is the slope of the saturated water vapor pressure curve (kPa℃\u003csup\u003e-1\u003c/sup\u003e), \u0026gamma; is the dry‒wet gauge constant (kPa\u0026deg;C\u003csup\u003e-1\u003c/sup\u003e), and R\u003csub\u003en\u003c/sub\u003e is the surface net radiation (MJm\u003csup\u003e-2\u003c/sup\u003ed\u003csup\u003e-1\u003c/sup\u003e). The surface net radiation (R\u003csub\u003en\u003c/sub\u003e) is calculated from the net surface shortwave radiation R\u003csub\u003ens\u003c/sub\u003e and the net surface longwave radiation R\u003csub\u003enl\u003c/sub\u003e. The net surface shortwave radiation Rns is calculated according to Formula (3) as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"675\" height=\"100\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere R\u003csub\u003eso\u003c/sub\u003e is the extraterrestrial radiation (MJm\u003csup\u003e-2\u003c/sup\u003eday\u003csup\u003e-1\u003c/sup\u003e), n is the actual sunshine hours (h), N is the maximum possible sunshine hours (h), the coefficient a\u003csub\u003es\u003c/sub\u003e is the transmission coefficient of the extraterrestrial radiation reaching the ground on cloudy days, and a\u003csub\u003es\u003c/sub\u003e+b\u003csub\u003es\u003c/sub\u003e is the transmission rate of the extraterrestrial radiation reaching the ground on sunny days. The monthly a\u003csub\u003es\u003c/sub\u003e and b\u003csub\u003es\u003c/sub\u003e values are calculated according to the national standard of the \u0026apos;dry‒wet\u0026apos; climate grade (MAO fei et al., 2017).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;By calculating the AI and referring to the classification method of the dry‒wet climate proposed by Zhang et al. (2021), the climate zone was classified into 6 grades, namely, a superhumid area, humid area, subhumid area, subarid area, arid area and extremely arid area. The specific classification criteria are shown in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Grades of dry‒wet climate division\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.277777777777779%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.277777777777779%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.756944444444445%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.979166666666666%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eClimate zone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSuperhumid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eHumid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003eSubhumid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSubarid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\"\u003e\n \u003cp\u003eArid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.979166666666666%\"\u003e\n \u003cp\u003eExtremely arid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.625%\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026lt;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e0.5~1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.277777777777779%\"\u003e\n \u003cp\u003e1.0~1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e1.5~3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.756944444444445%\"\u003e\n \u003cp\u003e3.5~20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.979166666666666%\"\u003e\n \u003cp\u003e\u0026ge;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Other research methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nonparametric Theil-Sen median estimation and Mann-Kendall test were used to estimate the linear trend. Sen\u0026apos;s slope estimation is a nonparametric test method proposed and developed by Sen in 1968 (Sen, 1968). Zhang et al. (2000) estimated the trend slope of N to the data in n samples (Jiang et al., 2015). When the calculation result is greater than 0, the time series to be analyzed shows an upward trend. When the calculation result\u0026nbsp;is less than 0, the time series to be analyzed shows a downward trend.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Mann-Kendall test was used to detect the mutation of the annual average AI value in Xinjiang. The Mann-Kendall test is a nonparametric test method. Compared to parametric test methods, the Mann-Kendall test does not require the sample to follow a certain distribution and is not affected by the presence of a few outliers. This test is thus more suitable for sequential variables (Fensholt, 2012 and Yue, 2002). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this paper, Morlet wavelet analysis is used to study the periodicity of the dry‒wet changes.The wavelet transform method has the characteristics of multiresolution analysis and has the ability to characterize the local characteristics of a signal in both the temporal and frequency domains. By decomposing a time series into the time-frequency domain, the significant fluctuation pattern of the time series can be obtained; that is, the periodic change dynamics and the time pattern of the periodic change dynamics can be determined (Morlet, 1982 and Torrence et al., 1998). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis paper also uses the natural orthogonal function (EOF) decomposition method is used to study the distribution characteristics of dry‒wet spatial anomalies in Xinjiang.Empirical orthogonal function (EOF) analysis, also known as eigenvector analysis, is a method used to analyze the structural characteristics of matrix data and extract the main data features. Also called principal component analysis, it is the most commonly used linear dimension reduction method. The goal of this analysis method is to map high-dimensional data into a low-dimensional space through some linear projection while expecting to maximize the amount of information (variance) of the data in the projected dimension to allow fewer data dimensions to be used while retaining as many original data points as possible. The EOF analysis method can decompose a temporally varying variable field into a spatial function component that does not change temporally and a temporal function part that depends only on time and does not change spatially (Pang et al., 2014; Cai et al., 2022). \u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Temporal characteristics of the dry‒wet climate in Xinjiang in the last 60 years\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1961 to 2020, the annual AI of Xinjiang showed a significant downward trend, decreasing by 2.1 per decade (passing the 0.05 significance test); this result reflects the fact that the climate in Xinjiang has become more humid over the past 60 years. The aridity index showed a turning point in the late 1980s; the values were high in the early stage and low in the late stage, indicating that the climate in Xinjiang was relatively dry before the late 1980s and relatively wet after this period. The year 2020 was the wettest year in Xinjiang in the past 60 years, with an annual aridity index of 4.71, while 1968 was the driest year in Xinjiang in the past 60 years, with an annual AI of 49.67. In terms of the annual trend, the AI showed a decreasing trend year-by-year. The AIs of each decade were 26.44 in the 1960s, 22.76 in the 1970s, 18.63 in the 1980s, 16.03 in the 1990s, 15.30 in the 2000s and 13.59 in the 2010s(Fig. 2).\u003c/p\u003e\n\u003cp\u003eXinjiang has a vast territory, and the climate difference between southern and northern Xinjiang is large. Therefore, the Xinjiang Geographical Division divides Xinjiang into northern Xinjiang, the Tianshan Mountains and southern Xinjiang. The annual AI trends in northern Xinjiang, Tianshan Mountains, and southern Xinjiang are consistent with those in Xinjiang overall, showing significant downward trends, that is, consistent wetting trends, with decline rates of 0.42, 0.22 and 4.58/10 a, respectively. The annual AI values of northern and southern Xinjiang decreased each year, while that of the Tianshan Mountains showed a decreasing trend with interdecadal fluctuations; in the three regions, the AIs decreased by 34%, 33% and 56%, respectively, in the 2010s compared to the 1960s. Among the three subregions, the southern Xinjiang region is the most humid, followed by northern Xinjiang and then the Tianshan Mountains. The wettest year in northern Xinjiang was 2016, with an annual aridity index of 2.64, and the driest year was 1962, with an annual aridity index of 8.84. The wettest year in the Tianshan Mountains was 1998, with an annual aridity index of 1.63, and the driest year was 1985, with an annual aridity index of 6.17. The wettest year in southern Xinjiang was 2020, when the annual aridity index was 2.86; the driest year was 1968, when the annual aridity index was 98.41.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the perspective of the spatial variation in the annual drought index corresponding to the Sen trend results in Xinjiang, the annual aridity index in Xinjiang has shown a downward trend over the past 60 years; that is, it has shown a wetting trend, especially in the southern Basin and eastern Xinjiang. This wetting trend is most obvious, and the aridity index decreases by more than 5 per decade on average, among which those in the Bayingolin Mongol Autonomous Prefecture and some areas of Turpan City decreased by 8-16. The aridity index values in most areas of northern Xinjiang decreased by 0.05~5 per decade. This conclusion is similar to the previous conclusions obtained from analyses of drought indices such as the meteorological drought composite index (MCI), Palmer drought severity index (PDSI) and precipitation index (Wu et al., 2022; Guo et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe significance test showed that 83% of the stations in Xinjiang recorded significant downward trends in the annual AI passing the 0.05 significance test. That is, most of Xinjiang became significantly wetter. Only the southern Ili Valley, Tacheng, Turpan, Hotan and southern Bayingolin Mongol Autonomous Prefecture did not pass the 0.05 reliability test (Figure 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Spatial distribution characteristics of AI climate zones in Xinjiang\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the distributions of the average AI and average precipitation from 1961 to 2020, the climate zone divisions obtained from the aridity index results in Xinjiang are consistent with the regional boundaries divided by precipitation (Figure 4). Most of northern Xinjiang and the western part of southern Xinjiang receive precipitation totals between 100 and 200 mm, and the average annual aridity index in these regions is less than 20. Most of these areas are arid and subarid. Among them, the average annual precipitation totals in some areas of the Ili River Valley and the southern mountainous areas of Urumqi, Tianchi and other Tianshan Mountains are greater than 300 mm, and the average annual aridity index is less than 3.5, corresponding to subarid and subhumid areas. The average annual precipitation totals in the southern Xinjiang basin and the eastern Xinjiang region are mostly less than 100 mm and, in some areas, even less than 50 mm, indicating perennially arid areas. The average annual aridity index values here are greater than 20, suggesting extremely arid areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Interdecadal variability characteristics of dry\u003c/strong\u003e\u003cstrong\u003e‒\u003c/strong\u003e\u003cstrong\u003ewet climate zones in Xinjiang\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTaking the mean value of the aridity index from 1961 to 1990 as the base period, the AI anomalies in the Tacheng area, northern Altay area, Tianshan Mountain area, western southern Xinjiang, Bayingolin Mongol Autonomous Prefecture, and eastern Xinjiang region in the 1960s were positive, indicating drier conditions than those in the base period, especially in the southern part of Bayingolin Mongol Autonomous Prefecture and the local area of Hami City. In the 1970s, most of Xinjiang still had positive anomalies, with drier conditions than the base period. Compared to the 1960s, the driest area began to move to the Hotan area in the southern Tarim Basin, and most of the Tianshan Mountains and eastern Xinjiang had negative anomalies, with wetter conditions than the base period. Since the 1980s, most areas of Xinjiang have become wetter, with significantly increased wet conditions compared to the previous two years. That is, the western part of southern and northern Xinjiang and the eastern part of the Tarim Basin had negative anomalies, while the eastern part of Changji Prefecture, the western part of Hotan and most of Kashgar were dry areas. In the 1990s, the aridity index values in most areas of Xinjiang exhibited negative anomalies, with significantly wetter conditions than those in the base period, especially in the southern Xinjiang Basin and most parts of eastern Xinjiang. In the 2000s and 2010s, most of northern Xinjiang, the Tianshan Mountains, Hami City in eastern Xinjiang, Kashgar, Kizilsu Kirghiz Autonomous Prefecture and Hotan in western southern Xinjiang were wet, especially in southern Xinjiang and eastern Xinjiang. With further wetting, only the eastern part of Changji Prefecture was a dry area. In general, the dry‒wet changes in Xinjiang reversed between the 1980s and the 1990s. Before this period, most of Xinjiang showed a drying trend. Since the 1980s, Xinjiang has tended to be wet, and this trend is more pronounced in the southern Xinjiang region(Figure 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe AI sequence of the Xinjiang region was calculated according to the classification standard of 6 dry‒wet grades adopted in this paper. There are 5 kinds of climate zones in Xinjiang, including the extremely arid region, arid region, subarid region, subhumid region and humid region. From the average value during the 1961-2020 period, the station ratio of the arid climate zone (including extremely arid, arid and subarid areas) in Xinjiang was 93%, while that of the humid climate zone (including subhumid and humid areas) was only 7% and was distributed mainly in the Tianshan Mountains.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the 1961-2020 period, the total station ratio of extremely arid and arid areas decreased annually; overall, the number of stations in the extremely arid area decreased from 38% in the 1960s to 14% in the 2010s. The ratio of stations in arid areas fluctuated, with the lowest observed in the 1960s (45%) and the highest observed in the 2000s (52%). The total station ratio of subarid, humid and semihumid areas increased from 12% in the 1960s to 32% in the 2010s, and the station ratio of humid and subhumid areas increased from 4% in the 1960s to 7% in the 2010s. The subarid, humid and semihumid areas showed significant increasing trends, the extremely arid area showed a significant decreasing trend, and the arid area showed fluctuating changes. It can also be seen that the Xinjiang region as a whole showed a wetting trend (Figure 6).\u003c/p\u003e\n\u003cp\u003eBy analyzing the station ratios of different climate zones in each subregion, it can be seen that the climate zones included in northern Xinjiang are mainly arid and semiarid regions. The station ratio of arid climate zones identified in each decade decreased annually, while that of subarid regions increased annually. The station ratio of arid regions decreased from 32% in the 1960s to 14% in the 2010s, while that of subarid regions increased from 8% in the 1960s to 26% in the 2010s. The climate zones in the Tianshan Mountains mainly include arid, subarid, humid and subhumid areas. From the perspective of decadal changes, the humid and subhumid areas, subarid areas in the Tianshan Mountains showed increasing trends, increasing by 4% and 1%, respectively, from the 1960s to the 2010s, while the arid area showed a decreasing trend, decreasing by 4% from the 1960s to the 2010s. The southern Xinjiang region contains only extremely arid and arid climate zones. Since the 1960s, the extremely arid climate zone area in this region has shown a significant decreasing trend, while the arid climate zone area has shown a significant increasing trend. From the 1960s to the 2010s, the station ratio in the arid zone increased by 2 times (16%), while that in the extremely arid climate zone decreased by 70% (24%). In general, from the perspective of the decadal changes from 1960-2020, the station ratios of the humid climate zones in the three subregions of Xinjiang increased, while the proportion of the arid climate zone decreased. It can also be seen from Table 1 that each subregion showed a significant wetting trend, especially since the 21\u003csup\u003est\u003c/sup\u003e century.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 Ratios of stations in different climate zones of Xinjiang in each decadal period (%)\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"492\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.663951120162933%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55193482688391%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.775967413441956%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1960s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1970s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1980s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2000s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2010s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"14.663951120162933%\"\u003e\n \u003cp\u003eNorthern Xinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55193482688391%\"\u003e\n \u003cp\u003eSubarid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.775967413441956%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.911694510739856%\"\u003e\n \u003cp\u003eArid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.455847255369928%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"14.663951120162933%\"\u003e\n \u003cp\u003eTianshan Mountains\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55193482688391%\"\u003e\n \u003cp\u003eHumid and subhumid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.775967413441956%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.911694510739856%\"\u003e\n \u003cp\u003eSubarid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.455847255369928%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.911694510739856%\"\u003e\n \u003cp\u003eArid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.455847255369928%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"14.663951120162933%\"\u003e\n \u003cp\u003e\u0026nbsp;Southern Xinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55193482688391%\"\u003e\n \u003cp\u003eArid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.775967413441956%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.201629327902241%\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.911694510739856%\"\u003e\n \u003cp\u003eExtremely arid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.455847255369928%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.126491646778042%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Analysis of the dry\u003c/strong\u003e\u003cstrong\u003e‒\u003c/strong\u003e\u003cstrong\u003ewet climate cycle and mutation detection in Xinjiang\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the AI wavelet analysis results in Xinjiang shown in Fig. 7(a), the original AI curve changed in a disorganized way, and no obvious periodicity can be seen. Through Morlet wavelet analysis, it can be clearly seen from Figs. 7 (b) and (c) that the periodicity of the dry‒wet changes in Xinjiang is significant, mainly with 2.5-year and 6-year periodic changes, and the confidence of both periods exceeds 95%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to the abrupt AI change detection results obtained in Xinjiang over the past 60 years in Figure 8, the UB and UF curves intersected in 1986, and the UF curve passed the critical line of the 0.05 level in 1992; that is, the abrupt change in the wetting trend in Xinjiang reached significance in this year, and then the AI basically maintained a decreasing trend only in 1997, 2001, 2019 while again increasing in the other individual years. Thus, Xinjiang has experienced a continuous wetting process since 1986.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Analysis of the spatial and temporal characteristics of the dry\u003c/strong\u003e\u003cstrong\u003e‒\u003c/strong\u003e\u003cstrong\u003ewet climate in Xinjiang\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further understand the spatial distribution characteristics of the dry‒wet climate in Xinjiang, the annual AI values at 99 stations were expanded using the empirical orthogonal function (EOF). The results showed that the first three eigenvectors were the main components, with variance contribution rates of 40.62%, 26.15% and 10.16%, respectively, and a cumulative contribution variance rate of 76.93%. The variance contribution rate of each feature vector after the third feature vector was small and thus could be ignored.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe first eigenvector (Fig. 9a) values of the EOF decomposition were negative in the whole Xinjiang region, representing the consistent change in the dry‒wet climate in Xinjiang; that is, the climate in Xinjiang was generally either dry or wet. The intensity of this change varied among different regions. The eastern and southern Xinjiang basins were the main large-value areas and the main control areas of the first mode. The time coefficient PC1 (Fig. 9b) corresponding to the first mode shows a clear, abrupt change over time, with the time coefficients for the 1961-1986 period being predominantly negative, indicating that the climate in Xinjiang was generally dry during this period, with the lowest coefficient observed in 1985, corresponding to the driest period in this region. Since 1987, the time coefficient has been dominated by a positive orientation, indicating that the whole climate in Xinjiang has been wet after this period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe second eigenvector (Fig. 9c) differed significantly from the first eigenvector. The eigenvector values of the northern and southern Xinjiang basins were negative, while those of the Tianshan Mountains were positive, reflecting the opposite spatial distribution characteristics of the dry‒wet climate in northern and southern Xinjiang compared to those in the Tianshan Mountains. The negative large-value area is located in the southeastern Tarim Basin, and the positive large-value area is located in Turpan. The time coefficient PC2 (Fig. 9d) corresponding to the second mode shows the same trend as the large PC1 value; that is, before 1987, the values were mainly negative, and since 1987, they were mainly positive.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third eigenvector (Fig. 9e) was positive in most parts of Xinjiang and negative only in southern Bazhou, Turpan and southern Hami, thus representing the consistent change in dry and wet conditions in most parts of Xinjiang, while Turpan, southern Hami and southern Bazhou showed the opposite change. The positive high-value areas of the modal eigenvector were located in Hotan and Kashgar. The time coefficient PC3 (Fig. 9f) corresponding to the third mode was reflected spatially in the alternation of heavy and light drought events in southern Bazhou, Turpan and Hami with most of western Xinjiang. The time coefficient was the smallest in 1974, thus accurately reflecting the severe drought events that occurred at this time in most of Xinjiang.\u003c/p\u003e"},{"header":"4. Conclusion and discussion","content":"\u003cp\u003eBased on the daily data of 99 meteorological stations in Xinjiang in the past 60 years (1961-2020), the potential evapotranspiration was calculated by using the modified FAO56 Penman-Monteith model. On this basis, the aridity index(AI) was calculated, and then the spatial and temporal evolution characteristics of dry and wet climate in Xinjiang were analyzed in detail by using interannual AI, which was helpful to better understand the regional and interannual changes of climate change in this area. The main conclusions are as follows :The main conclusions are as follows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom the average climatological AI state from 1961 to 2020, northern and southern Xinjiang were found to be arid and subarid regions, respectively. The Tianshan Mountains, including parts of the Ili River Valley and the southern mountainous areas of Urumqi, were found to be subhumid areas; and the rest of Xinjiang, especially the eastern and southern Xinjiang basins, was found to contain extremely arid areas. In the past 60 years, the annual AI of Xinjiang showed a significant downward trend, and the climate tendency rate was 2.4/10a; that is, the climate of Xinjiang has become wetter over the past 60 years. From the perspective of each subregion, the changes in northern Xinjiang, the Tianshan Mountains and southern Xinjiang all showed consistent wetting trends, especially in southern Xinjiang; in these subregions, the annual AI values decreased by 0.45, 0.23 and 5.23/10 a, respectively. According to the analysis of the station ratios of different climate zones from the 1960s-2010s, the total station ratio of arid and extremely arid areas in Xinjiang showed a decreasing trend, while the total station ratios of subarid areas and humid and semihumid areas showed increasing trends, especially since the 21\u003csup\u003est\u003c/sup\u003e century.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe wavelet analysis and abrupt change detection showed that the dry‒wet changes were most pronounced in a 6-year cycle, and Xinjiang has been in a continuous wetting process since 1986, with a significant abrupt change occurring in 1992. The first EOF decomposition mode was consistent in Xinjiang; that is, the climate in Xinjiang was generally either dry or wet. The intensity of this change differed among different regions. The eastern and southern Xinjiang basins were the main large-value areas, that is, the main control area of the first mode. The corresponding time coefficient also accurately reflects that the year of dry‒wet mutation in Xinjiang was 1986. The second mode reflected the opposite spatial distribution characteristics of dry‒wet climate change in southern and northern Xinjiang compared to the Tianshan Mountains. The third mode reflected that the dry‒wet changes were consistent among most areas of Xinjiang, though the eastern part of Xinjiang and the southern part of Bayingolin Mongol Autonomous Prefecture showed the opposite characteristics. From the interdecadal spatial evolution characteristics of the dry‒wet climate in Xinjiang, it can be seen that the dry‒wet climate trend reversed in the 1980s and 1990s. Before this period, Xinjiang\u0026apos;s climate was mainly dry. After this period, a wetting trend was observed compared to the base period. The dry area in the southern Xinjiang basin expanded slightly in the 2000s and shrank again in the 2010s, while the dry area expanded in the 2010s in the eastern Xinjiang.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe AI of each subregion in Xinjiang showed a decreasing trend with different degrees from 1961 to 2020; that is, the overall climate showed a \u0026apos;wetting\u0026apos; trend, consistent with most previous studies on dry‒wet changes in Xinjiang (Jiang et al., 2009; Zhang et al., 2010). Yao Junqiang et al. (2021) also pointed out that since the middle and late 1980s, the temperatures in Xinjiang have increased, and the precipitation amounts have also increased, showing the characteristics of \u0026quot;warming and wetting.\u0026quot; This study also pointed out that since 1997, the climate in Xinjiang has shown a strong signal change from \u0026quot;warm and humid\u0026quot; to \u0026quot;warm and dry.\u0026quot; After this year, the AI remained at a high level but still showed a deceasing trend; thus, the climate of Xinjiang was still becoming generally wetter. It is worth noting that although the area is affected by both precipitation and potential evapotranspiration, Xinjiang, as an important component of the arid region of Northwest China, also has a strong signal of rising temperatures and increasing precipitation. However, due to its geographical location far from the ocean and distantly inland, such strong warming is accompanied by vast potential evaporation, and the absolute value of the precipitation increase is thus not high (the annual precipitation increase rate in Xinjiang is only 10.14 mm/decade). Therefore, this observed trend is not expected to change the nature of Xinjiang as an arid region in the short term and is far from sufficient to produce qualitative changes in the regional dry‒wet conditions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimulations of the future climate change trends in Xinjiang and Northwest China show an overall wetting trend (Zhang et al., 2022; Feng et al., 2019). Feng et al. (2019) used the greenhouse gas scenario emission concentration provided by IPCC AR5 to simulate and test climate change in the northwest region from 1951 to 2015 and predict the climate change trend in the northwest region over the next 10 years. Their results show that by 2030, the annual average temperature in the northwest region is expected to show an increasing trend, with more complex precipitation change and an overall increase in aridity index. Zhang et al. (2022) calculated the regional aridity index (AI) of China from 2020 to 2099 by using projected model data and analyzed the dry‒wet climate change trends of China from 2020 to the end of this century under the RCP4.5 and RCP8.5 scenarios. They concluded that under both scenarios, the wetting trend in the western part of China will be more pronounced with larger range under the RCP8.5 scenario than under the RCP4.5 scenario. Based on 21 available CMIP5 models, Wang et al. (2020) showed that in the case of global temperature increases of 2 \u0026deg;C and 4 \u0026deg;C, the dry‒wet changes indicated by the AI showed that the drought area will increase most in semiarid areas, followed by in arid areas. Therefore, if the variation characteristics of the aridity index in different regions and times in Xinjiang continue in the future, the flood and drought events in northern and southern Xinjiang will also change greatly, thus critically impacting agriculture and society. In the future, the projected changes in the AI in Xinjiang will be analyzed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Xinjiang Natural Science Foundation (2022D01B181) and LCPS Youth Fund(2022).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. \u003cstrong\u003eXiulan Wu:\u003c/strong\u003eInvestigation,Methodology, Writing-original draft, Writing-review \u0026amp; editing.\u003cstrong\u003eCunjie Zhang:\u003c/strong\u003eMethodology, Data curation. \u003cstrong\u003eSiyan Dong:\u003c/strong\u003eSupervision,Conceptualization,Writing- review \u0026amp; editing. \u003cstrong\u003eJiahui Hu:\u003c/strong\u003eVisualization,Software. \u003cstrong\u003eXinyi Tong:\u003c/strong\u003eProject administration,Resources.\u003cstrong\u003eXiannian Zheng:\u003c/strong\u003eValidation,Software.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eChen F H, Huang W, Jin L Y, et al. 2011.Spatiotemporal precipitation variations in the arid Central Asia in the context of global warming. 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Sci.30,37-52.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dry‒wet climate change, Aridity index, Observations, Xinjiang","lastPublishedDoi":"10.21203/rs.3.rs-2689317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2689317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Precipitation change and dry‒wet trend are the most critical natural factors that determine the social development and civilization process in Xinjiang. The Aridity Index (AI), calculated from 99 homogeneous meteorological stations from 1961 to 2020,was used to analyze the variation of dry‒wet climate change in Xinjiang in the past 60 years . The results show that the annual AI in Xinjiang has shown a significant decreasing trend over the past 60 years; that is, the climate in Xinjiang, especially southern Xinjiang, has become wetter. The interdecadal variations from the 1960s-2010s show that the total station ratio of arid and extremely arid areas in Xinjiang showed a decadal decreasing trend, while the subarid areas and humid and semihumid areas showed increasing trends, especially since the beginning of the 21st century. The interdecadal spatial evolution characteristics show that the dry‒wet climate in Xinjiang reversed in the 1990s. An abrupt change in the annual aridity index occurred in 1986, after which the study region was basically in a continuous wetting process. The first empirical orthogonal function (EOF) decomposition mode is consistent in Xinjiang; that is, the climate in Xinjiang is generally dry or wet, and the intensity of this change varies among different regions. The second mode reflects the opposite spatial distribution characteristics of the dry‒wet climate in southern and northern Xinjiang with the Tianshan Mountains. Dry‒wet climate changes in Xinjiang mainly exhibit 2.5-year and 6-year oscillation periods, between which the 6-year period is more significant.","manuscriptTitle":"Spatiotemporal dynamics of the aridity index in Xinjiang over the past 60 years","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-17 14:43:37","doi":"10.21203/rs.3.rs-2689317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-05-13T10:24:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-05-10T21:36:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"881dd1d2-4e09-4d00-be3e-98f09df8c40d","date":"2023-05-07T03:30:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-05-06T22:33:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-03-14T05:58:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-14T05:58:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Earth Sciences","date":"2023-03-14T01:03:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b4d959af-f31d-49ac-92cc-1acb3d012b11","owner":[],"postedDate":"March 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:24:06+00:00","versionOfRecord":{"articleIdentity":"rs-2689317","link":"https://doi.org/10.1007/s12665-023-11070-3","journal":{"identity":"environmental-earth-sciences","isVorOnly":false,"title":"Environmental Earth Sciences"},"publishedOn":"2023-08-09 21:57:32","publishedOnDateReadable":"August 9th, 2023"},"versionCreatedAt":"2023-03-17 14:43:37","video":"","vorDoi":"10.1007/s12665-023-11070-3","vorDoiUrl":"https://doi.org/10.1007/s12665-023-11070-3","workflowStages":[]},"version":"v1","identity":"rs-2689317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2689317","identity":"rs-2689317","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}