The Hindu Kush Himalaya (HKH) is one of the world’s most climate-sensitive mountain regions, where the summer monsoon strongly influences water availability, agricultural production, and hydrometeorological hazards. High-resolution subseasonal-to-seasonal (S2S) prediction is therefore important for disaster risk reduction, water-resource management, and agricultural planning across the region. This dataset was generated using the seamless prediction system developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP, CAS). The system combines the FGOALS-f2 fully coupled climate model with dynamical downscaling using the FGOALS-UFS stretched-grid model, providing a horizontal resolution of approximately 20 km over the target region. The hindcast covers 2010-2024, with eight ensemble members for each initialization, while the 2026 real-time summer monsoon forecast comprises 49 forecasts. Each forecast extends to a lead time of 130 days. The dataset contains 54 meteorological variables, with a primary focus on precipitation and 2 m temperature, and is provided in NetCDF format at 3-hourly, 6-hourly, daily, dekadal, and monthly frequencies. An initial application to the 2026 summer monsoon indicates predominantly below-normal precipitation and temperature anomalies of 0.5-2 °C above normal across much of South Asia and the HKH, implying elevated risks of drought, heatwaves, and water stress, while localized heavy rainfall may still trigger floods, landslides, and glacial lake outburst floods. The dataset provides a high-resolution ensemble resource for S2S research, regional climate services, and risk-informed decision-making across the HKH.
| # | number | name | type |
| 1 | W2412139 | Formation Mechanism and Sub-seasonal Dynamic Prediction of Extreme Rainfall in the Southern Slopes of the Himalayan Region | National Natural Science Foundation of China |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.ncdc.db7809.2026
10.12072/ncdc.db7809.2026
