XJ is located in the arid region of Northwest China. The basins and mountains are distributed alternately in XJ, forming a unique and complex topographic structure. XJ has an arid climate with an average annual temperature of 10°C–15°C and an average annual precipitation of less than 150 mm (Li et al., 2011). It is highly sensitive to global warming and the ecological environment in this region is very fragile.

According to topography, XJ is divided by the Tianshan Mountains into northern and southern XJ, resulting in three major sub-regions: northern XJ, Tianshan Mountains, and southern XJ (Reziwanguli et al., 2016; Kang et al., 2018; Wu et al., 2020). Several studies have divided XJ into mountainous, oasis, and desert areas (Chen et al., 2005; Zhang et al., 2021), and obtained some beneficial conclusions about the climate of XJ. For extremely arid XJ, water is a fundamental natural and strategic economic resource, and it is also the lifeblood of XJ’s high-quality social and economic development. The watershed, as the main carrier of water resources in XJ, connects the natural elements in the watershed into a whole river. Human activities are primarily performed in watersheds. However, few studies have considered the watershed as a unit for conducting research on climate change in XJ. Based on the distribution of major watersheds and water systems in XJ, and also in terms of the expedition zones of the Third XJ Scientific Expedition and Research Program, we divided XJ into five sub-regions: the Irtysh River Basin (IRB), the economic belt on the northern slope of the Tianshan Mountains (NSTM), the Ili River Basin (ILRB), the Turpan-Hami Basin (THB), and the Tarim River Basin (TRB) (Figure 1). Within the IRB, the Irtysh River is the only river in China that flows into the Arctic Ocean. It originates from the southwestern slope of the Altai Mountains in China, and its water volume ranks second in XJ following the Ili River (Liu et al., 2017; Wang et al., 2022). The NSTM consists of small- and medium-sized rivers and is the most economically developed area in XJ, with the sharpest contradiction between the supply and demand of water resources (Sun et al., 2022). In the ILRB, the Ili River originates in the Tianshan Mountains and eventually joins Balkhash Lake, and is the river with the largest water volume in XJ (Liu et al., 2022). The THB is among the most arid basins in XJ and is rich in wind and solar energy but extremely limited in water resources (Fang et al., 2010). The TRB is located in southern XJ, between the Tianshan and Kunlun Mountains, and is the largest inland river basin in China (Xue et al., 2022).

The daily average temperature and precipitation data in XJ from 1960 to 2019 were provided by the China Meteorological Administration (CMA, http://data.cma.cn/en). To ensure the continuity of data, some meteorological stations with missing data on temperature and precipitation were excluded, and 80 meteorological stations were finally selected for the analysis in this study (Figure 1). Seventy-four atmospheric circulation indices were obtained from the National Climate Center of China Meteorological Administration (https://cmdp.ncc-cma.net/cn), with seven indices excluded from practical use due to missing data from June to September.

In this study, the variation trends in temperature and precipitation in XJ and its subregions over the past 60 years were analyzed using the linear regression method. The Mann-Kendall method was used to detect abrupt change years and tendencies of climate sequences, and R/S analysis was applied to predict future variations in temperature and precipitation (Ding et al., 2018; Fang et al., 2022). The Pearson correlation analysis method was used to analyze the correlation between the atmospheric circulation index and climate factors in XJ, and the atmospheric circulation index with strong positive and negative correlations was selected. Cross-wavelet analysis was used to analyze the periodic characteristics of the climate factors and atmospheric circulation indices.

The cross wavelet transform (XWT) highlights the interrelationship among temperature, precipitation, and atmospheric circulation indices in high-energy regions (Grinsted et al., 2004; Hu et al., 2021). Wavelet transform coherence (WTC) shows this interrelationship in the low-energy region. The thin black arcs in XWT and WTC indicate the cone of influence of the wavelet boundary effect, and the thick black solid line indicates a significant correlation between the two, which passed the red noise test with a confidence level of 95%. The symbol “←” (“→”) indicates that the climate and atmospheric circulation factors are negatively (positively) correlated. The symbol “↓” (“↑”) denotes that the climate factor is 90° ahead of (behind) the variations in the atmospheric circulation factor (Liu et al., 2021).

The annual mean temperature in XJ during 1960–2019 showed a significant increasing trend with a warming rate of 0.32°C/10 a (p < 0.01), which is higher than the average in China (Zhao et al., 2020) (Figure 2A). The results of the Mann-Kendall test showed that the temperature changed abruptly in 1994 (Figure 2C). After the abrupt change, the multi-year average temperature increased by 1.08°C. The cumulative anomaly analysis showed that the annual mean temperature experienced a decreasing trend from 1960 to 1996 and an increasing trend from 1997 to 2019, with 1996 being the turning point (Figure 2E). Considering the results of the two methods, the annual mean temperature in XJ was concluded to have changed abruptly in the mid-1990s.

The annual precipitation in XJ shows a significant increasing trend from 1960 to 2019 with a wetting rate of 9.24 mm/10 a (p < 0.01), which is slightly lower than the average in China (Zhao et al., 2020) (Figure 2B). The results of Mann-Kendall showed that an abrupt change in precipitation occurred in 1991 (Figure 2D). After the sudden change, the multi-year average annual precipitation increased by 29.48 mm. The cumulative anomaly analysis showed that the annual precipitation in XJ experienced a decreasing trend from 1960 to 1986 and an increasing trend from 1987 to 2019, with 1986 being the turning point (Figure 2F). Considering the results of the two methods, the annual precipitation was concluded to have changed abruptly in the mid-late 1980s.

Temperature and precipitation in XJ showed significant increasing trends in all seasons. The warming rates for the four seasons were 0.36°C/10 a, 0.24°C/10 a, 0.32°C/10 a and 0.37°C/10 a, respectively, all of which have passed the significance test at the 99% significance level (Table 1). The most significant warming trend in XJ occurred in winter. Among the subregions, the most significant warming trend was observed in the THB. In spring, the most significant warming occurred in the IRB; in summer, it was in the ILRB and THB; in autumn, it was in the THB; and in winter, it was in the IRB. The wetting rates in XJ in the four seasons were 1.82, 3.48, 2.11, and 1.84 mm/10 a, respectively. The wetting trends of all seasons passed the significance test at the 99% significance level (Table 2). The maximum increase in precipitation in XJ occurred in the summer. The subregion with the most significant increasing trend in annual precipitation was IRB. The increasing trend of precipitation was remarkable in the NSTM during spring, TRB during summer, and IRB during autumn and winter.

More than 98% of the stations in XJ showed warming and wetting trends from 1960 to 2019. The subregions with significant warming were concentrated in IRB, NSTM, ILRB, and THB (Figure 3A). The warming trend was most pronounced at the Shisanjianfang station of the THB, with a warming rate of 0.82°C/10 a. The Kuqa and Aketao stations in the TRB showed a decreasing trend in temperature with variation rates of −0.09°C/10 a and −0.03°C/10 a, respectively. The increase in precipitation was more pronounced in the IRB, NSTM, ILRB, and western regions of the TRB (Figure 3B). The most significant precipitation increase was observed in Urumqi on the NSTM, with a wetting rate of 24.95 mm/10 a. The precipitation in Shisanjianfang and Turpan stations in THB showed a decreasing trend, with variation rates of −2.86 and −0.31 mm/10 a, respectively.

Obvious regional differences were observed in seasonal warming in the sub-regions of XJ. In spring, summer, and autumn, the warming rate was the highest in the IRB. In winter, the greatest warming occurred mainly in the ILRB and THB. In terms of spatial differences, the most significantly warmed areas were mainly in the THB. The greatest warming rates were recorded at Shisanjianfang station in the THB (0.84°C/10 a and 0.90°C/10 a, respectively) in spring and autumn (Figure 4A,C); Hami (2.26°C/10 a) in summer (Figure 4B); and Fuyun station in the IRB (1.11°C/10 a) in winter (Figure 4D).

Regional differences in precipitation were also noted among the sub-regions of XJ. In summer, autumn, and winter, the largest wetting rates were observed in the ILRB. In summer, the sub-regions with the greatest precipitation increase were mainly in the ILRB and western TRB. In terms of stations, the most significant precipitation increase was mainly in the NSTM. In summer (Figure 5B), the largest wetting rate was observed at Daxigou station (12.89 mm/10 a); in spring (Figure 5A), at Tianchi station (10.75 mm/10 a); in autumn (Figure 5C), at Aheqi station (7.16 mm/10 a) in the TRB; and in winter (Figure 5D), at Yining (6.84 mm/10 a) in the ILRB.

The aforementioned analysis shows significant increasing trends of both annual mean temperature and annual precipitation in XJ and its subregions over the last 60 years. The results of the R/S analysis indicate that the Hurst indices of the annual mean temperature and precipitation in XJ and the subregions are both <0.5, presenting a decreasing trend in the future (Table 3). In the five subregions of XJ, the most significant decrease in annual mean temperature is predicted in the TRB, and the most significant decrease in annual precipitation is predicted in the THB. Analysis of the Hurst index indicates that warming and wetting in XJ will slow down in the future.

The key atmospheric circulation indices affecting climate change in XJ were selected by analyzing the correlations of temperature and precipitation with the atmospheric circulation indices (Table 4). The results showed that the subtropical high, polar vortex, Tibetan Plateau index, and other related circulation indices had significant effects on climate change in XJ. The indices with the most significant correlation and physical significance were selected. Temperature showed a significant and positive correlation with the area index of the subtropical high over North Africa, Atlantic, and North America (AISHNA) and its intensity index (IISHNA) (Figure 6A), whereas it was significantly and negatively correlated with the area index of the Asia polar vortex (AIAPV) and its intensity index (IIAPV) (Figure 6B). The annual precipitation showed a significant and positive correlation with the index of the Tibetan Plateau Region 1 (ITPR 1) (Figure 6C), and a significant and negative correlation with the intensity index of the Atlantic-European Polar Vortex (IIAEPV), the area index of the Northern Hemisphere Polar Vortex (AINHPV) and its intensity index (IINHPV) (Figure 6D).

Cross-wavelet analysis was performed to reveal the relationships between temperature/precipitation variations and key atmospheric circulation indices, such as subtropical high, polar vortex, and Tibet Plateau. Moreover, multi-timescale correlations between climate change and contemporaneous atmospheric circulation are discussed. The XWTs of temperature to AISHNA and IISHNA were consistent, both showing a significant resonance period at 1.5–4 a, which passed the significance test in 1980–1999 (Figures 7A, C). The WTC revealed that the significance of the two is greater in the low-energy region than in the high-energy region, and the coherence in 1981–2002 was extremely strong at the quasi-4a scale (Figures 7B, D). The XWT of temperature to IIPVA showed that they have a significant resonance period at the quasi-2a scale and have passed the significance test in 1964–1969 and 1981–1985 (Figure 7E). The XWT of temperature to AIPVA demonstrated a significant resonance period at 8–10 a scale, and they have a significant negative correlation. The WCT of the two showed extremely strong correlations at the 3–5 a scale for 1978–1988 and 1990–2001, and at the 6.5–14 a scale for 1978–2003 (Figures 7G, H).

A positive-phase quasi-2a resonance period existed in the high-energy region of the XWT between the precipitation variations in the XJ and ITPR 1 (Figure 8A). The XWT of the precipitation variation in XJ to AINHPV had resonance periods of 2–3 and 1–2.5 a in the high-energy region (Figure 8C) and 1–3 and 6–7 a in the low-energy region (Figure 8D). In addition, the XWT of the precipitation variation in XJ to IIPVNH had negative-phase resonance periods at 2.5–4 and 1–3 a in the high-energy region (Figure 8E) and at 1–3 a resonance period in the low-energy region (Figure 8F). The XWT of the precipitation variation in XJ to IIAEPV had negative-phase resonance periods at 2–4 and 1–3 a (Figure 8G). The above analysis further confirmed that the variations in temperature and precipitation in XJ were closely related to the anomalies of key circulation indices such as the subtropical high, polar vortex, and Tibet Plateau. These results could provide an important reference for the prediction of climate change in XJ.