Interannual climate anomalies modulate the subseasonal dynamical prediction skill from the regional perspective over Central Southwest Asia

The El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), as key oceanic boundary conditions, play a pivotal role in modulating regional climate variability. However, their influence on subseasonal dynamical prediction has yet to be fully understood. Focusing on Central Southwest Asia (CSWA), a region urgently needing accurate subseasonal prediction and significantly influenced by ENSO and IOD, this study investigates whether and how these interannual climate anomalies affect regional subseasonal rainfall prediction skills during early boreal winter using state-of-the-art Subseasonal-to-Seasonal (S2S) prediction models. First, the study finds that both deterministic and probabilistic prediction skills for domain-averaged rainfall anomalies and dry/wet events at a 2–4-week lead are significantly enhanced under La Niña and active IOD conditions compared to neutral states, while El Niño conditions show limited enhancement. This asymmetry in the ENSO impact is attributed to the inherent uncertainty in El Niño's influence on CSWA rainfall. Second, the analysis reveals that currently operational models exhibit higher skill in predicting ENSO at a 1-month lead, whereas predictions for IOD are comparatively less accurate. Nonetheless, prediction errors for both strong ENSO and IOD events at a 1-month lead are found to be significantly correlated with rainfall anomaly prediction errors over CSWA during the early boreal winter. This study confirms the significant effect of oceanic boundary conditions on regional subseasonal dynamical predictions and emphasizes the need to improve subseasonal prediction skills related to sea surface temperature variability associated with ENSO and IOD in order to reduce rainfall forecast errors and enhance the reliability of S2S predictions.