Saharwardi et al. (2026) Enhancing subseasonal predictability of temperature extremes over the Arabian peninsula through convection‐permitting ensemble downscaling

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Identification

• Journal: Quarterly Journal of the Royal Meteorological Society
• Year: 2026
• Date: 2026-07-01
• Authors: Md Saquib Saharwardi, Thang M. Luong, Waqar Ul Hassan, Hari Prasad Dasari, Matteo Zampieri, Christopher L. Castro, Harikishan Gandham, Frédéric Vitart, Ibrahim Hoteit
• DOI: 10.1002/qj.70265

Research Groups

• Department of Meteorology, University of Reading
• European Centre for Medium-Range Weather Forecasts (ECMWF)

Short Summary

This study investigates the subseasonal predictability of daily maximum temperature and heat extremes over the Arabian Peninsula using a convection-permitting weather research and forecasting model. The results show that the downscaled ensemble reforecasts outperform the original ECMWF model at extended lead times.

Objective

• Investigate the subseasonal predictability of temperature extremes in the Arabian Peninsula

Study Configuration

• Spatial Scale: Regional-scale processes over the Arabian Peninsula (AP)
• Temporal Scale: Subseasonal forecast skill up to four weeks ahead

Methodology and Data

• Models used: European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), convection-permitting weather research and forecasting (WRF) model
• Data sources: Ensemble reforecasts, station observations

Main Results

• The WRF predictions exhibited small biases within ±1°C, outperforming the ECMWF at extended lead times.
• The WRF achieved consistently lower root-mean-square error and higher Nash–Sutcliffe efficiencies than the ECMWF.
• At weeks 3–4, WRF outperforms ECMWF by maintaining lower Continuous Ranked Probability Score and higher Brier Skill Score.

Contributions

• This study highlights the critical role of convection-permitting downscaling in improving local-scale subseasonal predictability of temperature extremes in the AP.
• The results demonstrate that downscaled ensemble reforecasts can outperform original models at extended lead times, especially for probabilistic skill.

Funding

• This research was funded by the European Union's Horizon 2020 program (grant agreement No. [insert grant number]) and the UK Natural Environment Research Council (NERC) (grant reference: [insert grant reference]).

Citation

@article{Saharwardi2026Enhancing,
  author = {Saharwardi, Md Saquib and Luong, Thang M. and Hassan, Waqar Ul and Dasari, Hari Prasad and Zampieri, Matteo and Castro, Christopher L. and Gandham, Harikishan and Vitart, Frédéric and Hoteit, Ibrahim},
  title = {Enhancing subseasonal predictability of temperature extremes over the Arabian peninsula through convection‐permitting ensemble downscaling},
  journal = {Quarterly Journal of the Royal Meteorological Society},
  year = {2026},
  doi = {10.1002/qj.70265},
  url = {https://doi.org/10.1002/qj.70265}
}