Impact of thermal and dynamic spectral nudging on modeling of the 2022 Pakistan extreme rainfall event using multiscale processes

Climate change has led to an increase in the frequency of extreme precipitation events, posing significant risks. However, accurately modeling these events remains challenging. This study assessed the effects of spectral nudging with thermal (SN‐TQH) and dynamic (SN‐UV) variables on simulating precipitation at different timescales during the exceptional summer 2022 rainfall in Pakistan. The results indicate that spectral nudging significantly improves precipitation simulation, with SN‐TQH outperforming SN‐UV due to its reduced biases in representing the subtropical high‐pressure, trough, monsoon low‐pressure system (MLPS), and moisture conditions. Furthermore, the relative effectiveness of SN‐TQH and spectral nudging with all variables (SN‐ALL) is highly regime‐dependent. Specifically, SN‐TQH excels in large‐scale circulation‐driven events over southern Pakistan by accurately capturing the MLPS, Iranian high, and associated moisture convergence. Conversely, SN‐ALL outperforms in northern and national events by better controlling localized moisture convergence and mitigating vertical thermodynamic biases. Ultimately, these findings provide a scientific basis for selecting regime‐dependent nudging strategies, which can help improve the predictability of extreme precipitation and inform flood early‐warning systems in regions with complex topography.