Improved Identification of Extreme Rainfall Events in the Himalayan Foothills Using an Observation‐Based Ensemble Precipitation Dataset

Extreme rainfall events (EREs) like cloudbursts in the Indian Himalayas are not only unpredictable in location and timing but also catastrophic, often leading to severe flooding and landslides. Detecting them is challenging as they often occur in remote locations without gauge stations or radar coverage. The existing 0.25° station‐based deterministic product from the Indian Meteorological Department (IMD) systematically undercaptures these storms over the north‐western Himalayas. This study tests whether finer‐scale, terrain‐aware interpolation can improve ERE detectability without adding new stations. We evaluate the 0.1° Indian Precipitation Ensemble Dataset (IPED), developed using the same station network as the IMD dataset but with an improved interpolation technique, against three benchmark datasets: 0.25° IMD, the 0.1° IMERG satellite and the 0.12° IMDAA reanalysis dataset. Using state‐specific 99.90th percentile thresholds, the IMD dataset detects only 2, IMERG detects 3, while IPED captures 6 out of 24 EREs. Replacing the regional threshold with district‐specific 99.90th‐percentile values improves detection, with IPED capturing 7 out of 24 events, compared to 3 by IMD and 5 by IMERG. This study demonstrates that the new ensemble‐based precipitation dataset, combined with a district‐specific 99.90th percentile threshold, significantly enhances cloudburst‐like ERE detection compared to the other datasets, such as IMD 0.25°, IMERG 0.1° and IMDAA 0.12°. Hourly IMDAA fields corroborate that every IPED‐identified event spans a broad spatial footprint and persists for many hours, underscoring its hydrologic significance. The results show that (i) ensemble‐based interpolation alone can markedly improve ERE detection, and (ii) locally tuned thresholds are essential in a region where rainfall gradients are steep. The combination of IPED and district‐specific thresholds offers a practical, operationally viable framework for pinpointing cloudburst‐scale hazards in the Himalaya, outperforming widely used gauge, satellite, and reanalysis alternatives.