Indian summer monsoon precipitation and extremes over the Indian Himalayas in WRF dynamically downscaled (HARv2) reanalysis

Understanding the precipitation patterns in the western Himalayas (WH) during the Indian summer monsoon (ISM) is crucial across different spatiotemporal scales for societal well-being and effective risk management. This study conducts a comprehensive assessment of summer monsoon (June-September; JJAS) precipitation over WH using high-resolution simulations (at a 10 km grid-spacing) of the Weather Research and Forecasting model (referred to as WRF-HARv2), driven by ERA5 reanalysis for the period of from 1980 to 2020. The finding indicates that the dynamically downscaled WRF model provides a reliable spatial distribution of precipitation patterns during ISM against the ground-based gridded (IMD), satellite-based (GPM-IMERG), and reanalysis (ERA5) datasets. Empirical Orthogonal Function analysis/Principal Component Analysis was employed to investigate precipitation variability, and the results suggest that the WRF-HARv2 model exhibits potential skill in capturing the interannual precipitation variability over the WH. Our results also depict that the WRF-HARv2 effectively capture extreme precipitation events (EPEs) along with interannual variability, as observed in ERA5, indicating that decreasing the horizontal grid spacing of WRF to 10-km can reproduce extreme precipitation over WH. In examining extreme precipitation, finding shows that large amounts of moisture are being transported towards the WH, feeding the EPEs over WH, which is realistically represented by WRF-HARv2. Furthermore, WRF simulations reveal that EPEs over the WH are primarily driven by vertical advection in the moisture budget, with dynamic terms accounting for more than 98% of the moisture budget component. Overall, the analysis shows that WRF improves in representing the spatiotemporal variations of precipitation, interannual variability, and extreme precipitation, providing high-resolution climate information with more accurate comparisons to the observed dataset.