Flash Floods in High Mountain Asia in 2024: meteorological drivers and forecasting challenges

Precipitation-induced hazards are among the most destructive natural disasters worldwide disproportionately affecting developing countries where large rural populations reside in mountainous regions with limited infrastructure resilience. High Mountain Asia (HMA) is a global hotspot for precipitation induced hazards, and with climate change projected to intensify extreme precipitation events, both the frequency and severity of these events are expected to increase.

Flash floods and debris flows in HMA are primarily triggered by intense short-duration precipitation or prolonged rainfall, particularly during the summer monsoon season when soil moisture levels are already elevated. These rainfall events significantly increase the risk of hydrometeorological disasters, especially in steep and erosion-prone terrain. Due to their localised characteristics, hazards associated with heavy rainfall are particularly difficult to predict. The high-intensity and short-duration rainfall in particular impacts communities with minimal warning and limited time to respond. Improving the accuracy of high-intensity and /or extreme rainfall forecasts is therefore essential for reducing risk of disasters and strengthening early warning systems in the region.

This study evaluates the performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) short to medium-range raw ensemble (ENS) and the post-processed ecPoint rainfall forecasts in predicting high-impact precipitation events that triggered severe flash flooding across HMA. Case studies of extreme rainfall observed in Nepal, Afghanistan, India, and Pakistan during 2024 which led to severe and destructive flash floods are used to compare the performance of forecasts and their potential for early warning of hazards at different lead times. Each case study is analysed from a different perspective, emphasising the probabilities of rainfall thresholds being exceeded and triggering extreme hazards. By comparing these forecast systems, this study aims to assess their effectiveness in capturing extreme precipitation patterns and improving early warning capabilities in the region.