ISARD (v1.0) : A Reproducible Geostatistical Framework for Daily Precipitation Ensemble in Mountainous Terrain

Abstract. Gridded precipitation datasets are essential for hydrological and climate applications. However, commonly used products suffer from systematic biases such as seasonal total underestimations in mountainous regions and excessive smoothing of the spatial variability of extremes. Here, we present a reproducible workflow for generating a daily precipitation ensemble, conditioned on rain gauges, at 1 km resolution for mountainous regions. The approach leverages climatological information and spatial variability from Convection-Permitting Regional Climate Model (CP-RCM) simulations. The workflow corrects raingauge undercatch, incorporates CP-RCM-based climatology to improve seasonal totals, and estimates anisotropic variograms from CP-RCM daily fields to capture directional precipitation structures. Finally, Sequential Trans-Gaussian Simulations generate the daily ensemble of 100 members. We evaluate commonly used gridded precipitation products and the proposed approach using independent evaluation data, including in-situ measurements in mountainous areas (snow water equivalent, glacier mass balances, streamflow), regional catchment-scale water balance models, and hydrological models. Results demonstrate that our framework outperforms deterministic gridded products. First, it more accurately captures seasonal totals in highaltitude Snow Water Equivalent (SWE) and glacier observations, and reproduces both seasonal precipitation amounts and their interannual variability. Second, the daily ensemble captures fine-scale spatial variability and quantifies interpolation uncertainty, improving flood hydrological modelling. The workflow is fully reproducible via open-source code, transferable to regions with sparse rain-gauge networks or limited radar coverage. Beyond precipitation, it is adaptable to other climate variables simulated by weather models.