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December 9, 2025Frontiers in Plant Science3 citationsOpen Access

Projected hydroclimatic changes in Xinjiang under bias-corrected CMIP6 scenarios

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XCXin CongMBMengxin BaiJZJuncheng Zhang

Key Points

  • This research aims to understand hydroclimatic changes in Xinjiang using future climate projections.
  • Applied bias-corrected CMIP6 daily climate projections
  • Used BCCAQ method on ERA5 reanalysis and station data
  • Analyzed drought conditions and meteorological variables over 2031-2060
  • Significant warming trends across all scenarios
  • Increased potential evapotranspiration and aridification
  • Intensifying drought risk indicated by the SPEI index

Abstract

Understanding future hydroclimatic variability in arid regions is essential for sustainable development and climate adaptation. This study uses bias-corrected CMIP6 daily climate projections, derived by applying the BCCAQ method to ERA5 reanalysis and surface station data, to investigate the spatiotemporal evolution of key meteorological variables and drought conditions over Xinjiang during 2031–2060 under three SSP scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Results reveal significant warming trends across all scenarios, with stronger increases under high-emission pathways (up to 0.76°C/10a under SSP585), accompanied by enhanced potential evapotranspiration (PET) and widespread aridification. While precipitation shows an upward trend under SSP370 and SSP585, the warming-induced evaporative demand dominates, particularly in southern Xinjiang and the eastern basins. The SPEI index indicates an intensifying drought risk, with spatial patterns characterized by a “dry south–wet north” gradient and stronger basin aridification relative to mountainous regions. Moreover, this study highlights the physical mechanism linking temperature rise, enhanced PET, and intensified drought, providing robust empirical evidence for regional climate risk assessment and adaptation strategies in Central Asia. Despite methodological advantages, limitations associated with spatial resolution and structural uncertainty of GCMs persist, suggesting the need for integrating regional climate models (RCMs) and extreme event analyses in future research.

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Cite This Study

Cong et al. (2025) studied this question.

synapsesocial.com/papers/69401d5b2d562116f28f8bc1https://doi.org/10.3389/fpls.2025.1679735
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