Wang et al. (2025) Variation characteristics in compound drought-heatwave events in Northwest China and the relationship with sea surface temperature

Identification

• Journal: Theoretical and Applied Climatology
• Year: 2025
• Date: 2025-12-18
• Authors: Suyan Wang, Xin Li, Fan Wang, Dai Wang, Ying Huang, Hongjiang Xu
• DOI: 10.1007/s00704-025-05928-8

Research Groups

• Key Laboratory of Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, Yinchuan, China
• Ningxia Hui Autonomous Region Climate Center, CMA, Yinchuan, China

Short Summary

This study investigates the spatiotemporal variations and driving forces of compound drought-heatwave (CDHW) events in Northwest China, revealing asymmetric trends between its eastern and western regions. It finds that CDHW events are significantly influenced by sea surface temperature anomalies in the Indian and Pacific Oceans and the Atlantic Multidecadal Oscillation.

Objective

• To investigate the spatiotemporal variation discrepancies of compound drought-heatwave (CDHW) events in the eastern (ENC) and western (WNC) parts of Northwest China.
• To quantify the internal driving forces (contributions of drought, heatwave, and their coupling) to CDHW events.
• To analyze the relationship between CDHW events and sea surface temperature (SST) anomalies to elucidate external forcing mechanisms.

Study Configuration

• Spatial Scale: Northwest China, specifically divided into the eastern part (ENC, east of 97.25°E) and the western part (WNC, west of 97.25°E), encompassing Xinjiang, Gansu, Qinghai, Ningxia, and Shaanxi provinces.
• Temporal Scale: Daily data from 1961 to 2020 for meteorological observations, and 1962 to 2020 for atmospheric reanalysis and long-term trend analysis. Focus on CDHW events lasting three days or more during the warm season (May to October).

Methodology and Data

• Models used:
  • Standardized Precipitation Evapotranspiration Index (SPEI) on a 3-month scale (daily calculation).
  • Penman-Monteith equation for potential evapotranspiration (PET).
  • 90th percentile method for defining heatwave extremes.
  • Climate tendency and t-test for trend analysis and significance.
  • Mann–Kendall (MK) test for detecting abrupt changes.
  • Grey correlation analysis for quantifying the coupling effect between total heatwave and drought durations.
  • Multiple linear regression analysis for quantifying contributions of heatwave duration, drought duration, and their coupling to CDHW events.
  • Empirical Orthogonal Function (EOF) analysis for identifying dominant modes of CDHW events.
  • Pearson linear correlation coefficient for assessing relationships between time coefficients and SSTs.
  • 21-year sliding window approach for evaluating relationship stability.
  • Lanczos filter for separating interannual variations.
  • Wave activity flux (Takaya and Nakamura, 2001) for characterizing atmospheric wave propagation.
• Data sources:
  • Daily meteorological data (precipitation, mean/maximum/minimum temperature, wind speed, atmospheric pressure, water vapor pressure, sunshine duration, relative humidity) from 265 national meteorological stations, provided by the National Meteorological Information Center of the China Meteorological Administration (1961–2020).
  • Monthly atmospheric circulation reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 (1962–2020), with a spatial resolution of 1°×1°. Variables include geopotential height (at 200 hPa, 500 hPa, and 850 hPa), horizontal wind components (u and v), and integrated water vapor flux.
  • Monthly sea surface temperature (SST) index data from the National Climate Center under the China Meteorological Administration, including Indian Ocean Warm Pool Area Index (IOWP-AI), Indian Ocean Basin-Wide Mode Index (IOBW), Tropical Indian Ocean Dipole Index (TIOD), Subtropical South Indian Ocean Dipole Index (SIOD), Niño indices, Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Oyashio Current SST Index (OYI), and West Wind Drift Current SST Index (WWDI).

Main Results

• The frequency, individual duration, and total duration of CDHW events in Northwest China exhibit a tripolar distribution: high in the eastern region (east of 100°E), low in the central region (88–100°E), and high again in the western region (west of 88°E).
• In the Eastern part of Northwest China (ENC), these CDHW characteristics show a significant increasing trend, with an abrupt change around 1997. Post-1997, the annual mean frequency increased 2.3-fold, and the total duration increased approximately 2.5-fold. The rapid increase in total heatwave duration is the primary driver of CDHW events in ENC, contributing over 50% to the total duration in most areas, and up to 96.6% east of 100°E.
• In the Western part of Northwest China (WNC), CDHW characteristics demonstrate decreasing trends without any abrupt changes, although a shift from a decreasing to an increasing trend in annual mean frequency was observed around 1994. The reduction in total drought duration is a major contributor to the decrease in total CDHW duration in WNC, accounting for over 40% in most cases.
• Across most of Northwest China, total heatwave duration exhibits an increasing trend, while total drought duration shows a decreasing trend, yet they maintain a significant positive interdependence.
• The coupling effect (ξ) between total heatwave duration and total drought duration contributes less than 40% to the total duration of CDHW events in both ENC and WNC.
• Relationship with SSTs:
  • The monopolar mode (EOF1) of CDHW events is significantly related to Indian Ocean SSTs (IOWP-AI, IOBW, TIOD, SIOD) from January to April. An increase in Indian Ocean SST can trigger an anomalous anticyclone over Northwest China, favoring CDHW events, with this relationship strengthening since the mid-1990s.
  • The east-west dipole mode (EOF2) did not show a consistently high correlation with any specific global SST region.
  • The north-south dipole mode (EOF3) is linked to Pacific SST anomalies (most Niño indices, IOWP-II, IOBW) from January through May for its interannual variability. Its interdecadal variability is associated with the Atlantic Multidecadal Oscillation (AMO) from January to August (correlation coefficient of -0.47), which strengthens the Mongolian anticyclonic anomaly and affects this dipole mode, particularly intensifying since the mid-1990s.

Contributions

• Provides a novel investigation into the spatiotemporal variation discrepancies of CDHW events in the eastern and western parts of Northwest China using daily SPEI and percentile methods.
• Quantifies the distinct contributions of heatwave duration, drought duration, and their coupling to CDHW events in different sub-regions, identifying the dominant intrinsic drivers.
• Elucidates the external forcing mechanisms by analyzing the teleconnections between dominant CDHW modes and SST anomalies in the Indian Ocean, Pacific Ocean, and Atlantic Multidecadal Oscillation.
• Enhances the scientific understanding of CDHW events in a globally recognized ecologically fragile region, offering valuable insights for climate change adaptation strategies, monitoring, and prediction.
• Identifies a "tripole" characteristic in CDHW events across Northwest China, highlighting areas for future in-depth research.

Funding

• National Natural Science Foundation of China (42265006, 42230611)
• Innovation and Development Project of the China Meteorological Administration (CXFZ2023J052)
• Ningxia Science and Technology Innovation Team (2024CXTD006)
• Central Guiding Local Science and Technology Development Fund of Shandong—Yellow River Basin Collaborative Science and Technology Innovation Special Project (YDZX2023019)

Citation

@article{Wang2025Variation,
  author = {Wang, Suyan and Li, Xin and Wang, Fan and Wang, Dai and Huang, Ying and Xu, Hongjiang},
  title = {Variation characteristics in compound drought-heatwave events in Northwest China and the relationship with sea surface temperature},
  journal = {Theoretical and Applied Climatology},
  year = {2025},
  doi = {10.1007/s00704-025-05928-8},
  url = {https://doi.org/10.1007/s00704-025-05928-8}
}