Upadhyaya et al. (2026) Intensifying Heatwave Extremes in the Northwest Himalayas, India: Trends, Teleconnections, Causal Drivers, and CMIP6 Projections

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-10-11
• Authors: Anup Upadhyaya, Abhishek Kumar Rai
• DOI: 10.1002/joc.70615

Research Groups

Information not available from the abstract.

Short Summary

This study investigates spatio-temporal heatwave trends and temperature extremes in the Northwest Himalayas from 1980 to 2022, identifies key atmospheric-oceanic and land surface drivers, and projects future changes using CMIP6 models. It reveals significant warming, particularly at higher elevations, with snow-albedo feedback and upper-atmospheric circulation as primary drivers, and forecasts continued intensification of extreme warm events.

Objective

• To investigate spatio-temporal variations and long-term trends of heatwave indices and temperature extremes across the Northwest Himalayas (NWH) and its three major basins from 1980 to 2022.
• To examine relationships between hot days (TX90p) and key atmospheric-oceanic drivers (geopotential height, outgoing longwave radiation, sea surface temperature, land surface variables, and major teleconnection indices) using spatial correlation and causal discovery frameworks.
• To derive future projections of temperature extremes from 13 bias-corrected CMIP6 models under four Shared Socioeconomic Pathway (SSP) scenarios.

Study Configuration

• Spatial Scale: Northwest Himalayas (NWH) of India, including three major basins (Qura-Qush basin specifically mentioned).
• Temporal Scale: Historical analysis from 1980 to 2022; Interdecadal analysis for 2013–2022; Future projections up to the end of the century under four SSP scenarios.

Methodology and Data

• Models used: Ensemble of 13 bias-corrected CMIP6 models (for future projections).
• Data sources: Geopotential height (at 200 hPa and 500 hPa), outgoing longwave radiation (OLR), sea surface temperature (SST), land surface variables (e.g., snow cover), and major teleconnection indices. Specific data source types (e.g., satellite, reanalysis) for these variables are not detailed in the abstract.
• Methods: Spatial correlation analysis, PCMCI+ causal discovery framework.

Main Results

• Statistically significant increasing trends in temperature extremes were observed, particularly over higher-elevation regions, with the Qura-Qush basin exhibiting the strongest warming.
• The interdecadal analysis showed that 2013–2022 experienced the highest heatwave frequency, number, and duration across all basins.
• Spatial correlation analysis revealed strong associations between hot days (TX90p) and upper-atmospheric circulation at both 200 hPa and 500 hPa.
• PCMCI+ causal analysis identified snow cover as the strongest direct driver of hot days through snow-albedo feedback, followed by geopotential height at 200 hPa.
• No direct ENSO-TX90p linkage was detected, but indirect pathways were identified through ocean–atmosphere coupling, particularly via SST-driven modulation of upper-tropospheric circulation.
• Future projections indicate continued intensification of temperature extremes under all SSP scenarios, with divergence becoming pronounced by mid-century.
• By the end of the century, warm nights (TN90p) are projected to reach 31.47% under SSP1-2.6 to 75.72% under SSP5-8.5, while hot days (TX90p) are projected to range from 29.27% to 70.22%.
• Warm nights consistently show stronger increasing trends than hot days across all future scenarios.

Contributions

• Provides a comprehensive spatio-temporal analysis of heatwave indices and temperature extremes in the Northwest Himalayas, a cryosphere-sensitive region.
• Utilizes the PCMCI+ causal discovery framework to identify direct drivers of hot days, highlighting the critical role of snow-albedo feedback and upper-atmospheric circulation.
• Quantifies future projections of temperature extremes for the region using an ensemble of 13 bias-corrected CMIP6 models under multiple SSP scenarios.
• Reveals that warm nights are projected to intensify more significantly than hot days across all future scenarios, underscoring a specific vulnerability.

Funding

Information not available from the abstract.

Citation

@article{Upadhyaya2026Intensifying,
  author = {Upadhyaya, Anup and Rai, Abhishek Kumar},
  title = {Intensifying Heatwave Extremes in the Northwest Himalayas, India: Trends, Teleconnections, Causal Drivers, and CMIP6 Projections},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70615},
  url = {https://doi.org/10.1002/joc.70615}
}