Enhancing subseasonal predictability of temperature extremes over the Arabian peninsula through convection‐permitting ensemble downscaling

Abstract In a warming climate, the increasing frequency and severity of temperature extremes across the Arabian peninsula (AP) demand skillful prediction several weeks in advance for effective risk management. However, current coarse‐resolution models constrain subseasonal forecast skill to adequately resolve regional‐scale processes and extremes. Here, we examine the subseasonal predictability of daily maximum temperature ( T max ) and heat extremes over the AP by dynamically downscaling ensemble reforecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) using a convection‐permitting weather research and forecasting (WRF) model. An 11‐member WRF ensemble reforecast at 4‐km resolution was generated for the period 1998–2017 over the AP with lead times extending up to four weeks. We assessed the performance of WRF‐ downscaled T max relative to the ECMWF by comparing them against station observations. The WRF predictions exhibited small biases within ±1°C, whereas the ECMWF exhibited pronounced cold biases exceeding −2°C. Moreover, the WRF achieved consistently lower root‐mean‐square error and higher Nash–Sutcliffe efficiencies, whereas both models maintained strong correlations with the observations. Beyond week–2, the results revealed that the WRF experienced slower skill degradation than the ECMWF, highlighting its robustness at extended lead times. At weeks 3–4, WRF outperforms ECMWF by maintaining lower Continuous Ranked Probability Score and higher Brier Skill Score, demonstrating better probabilistic skill at subseasonal lead times. WRF also outperforms bias‐corrected ECMWF at subseasonal lead times. The ECMWF largely underestimated the hot‐day frequencies (˜30 days), while the WRF reduced this bias by a factor of nearly three (±10 days). In addition, WRF achieved higher hit rate and maintained greater reliability at longer leads (weeks 3–4) than ECMWF. WRF, however, show limitations in capturing T max and hot days along the coasts of the Red Sea. The findings of this study highlight the critical role of convection‐permitting downscaling in improving the local‐scale subseasonal predictability of temperature extremes in the AP.