In the Himalayan regions of complex terrains, such as Himachal Pradesh, the occurrence of extreme rainfall events (EREs) has been increasing, triggering landslides and flash floods. Investigating the dynamics and precipitation characteristics and improving the prediction of such events are crucial and could play a vital role in contributing to sustainable development in the region. This study employs a high-resolution numerical weather prediction framework, the weather research and forecasting (WRF) model, to deeply investigate an ERE which occurred between 8 July and 13 July 2023. This ERE caused catastrophic floods in the Mandi and Kullu districts of Himachal Pradesh. The WRF model was configured with nested domains of 12 km and 4 km horizontal grid resolutions, and the results were compared with global high-resolution precipitation products and the fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis dataset. The selected case study was amplified by the synoptic scale features associated with the position and intensity of the monsoon trough, including mesoscale processes like orographic lifting. The presence of a western disturbance and the heavy moisture transported from the Arabian Sea and the Bay of Bengal both intensified this event. The model has effectively captured the spatial distribution and large-scale dynamics of the phenomenon, demonstrating the importance of high-resolution numerical modeling in accurately simulating localized EREs. Statistical evaluation revealed that the WRF model overestimated extreme rainfall intensity, with the root mean square error reaching 17.33 mm, particularly during the convective peak phase. The findings shed light on the value of high-resolution modeling in capturing localized EREs and offer suggestions for enhancing disaster management and flood forecasting.

Point-wise intensification of daily rainfall extremes over India is well established, yet whether their joint spatial behavior is also changing has not been tested. We address this with 75 years (1951–2025) of 0.25° gridded India Meteorological Department daily rainfall, quantifying June–September (JJAS) extremal dependence through the extremal-coefficient function θ(h) and the dependence range ρ of a Brown–Resnick curve fitted to F-madogram estimates on rank-transformed fields; the rank transform strips out marginal trends, so any surviving signal belongs to the joint structure. The all-India range over the full record is 65.6 km (95% CI 62.0–69.5 km). Across 21 sliding 15-year windows, θ at 50 and 100 km fell significantly (p = 0.033 and 0.010 after serial-correlation correction): extremes co-occur more strongly at the storm scale. The signal is regional: Peninsular India’s dependence range grew from 56 km to 76 km between the 1951–1975 and 2001–2025 blocks, a 36% expansion (p = 0.002), and Central India’s window trend is +1.7 km decade⁻¹ (p = 0.017). Independently, the largest connected daily exceedance cluster expanded in area by roughly 40% (p ≤ 0.0006). A Pettitt test dates the step in the footprint series to 1986 (p ≤ 0.0001), whereas the dependence series show progressive trends without a detectable break. Spatial reorganization is compounding marginal intensification: across Peninsular India, joint flood risk is growing faster than cell-wise return levels indicate.