Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Karakoram Himalaya

Abstract. This study introduces a novel approach to post-processing (i.e., downscaling and bias-correcting) reanalysis-driven regional climate model daily precipitation that is capable of generalising to ungauged mountain locations by leveraging sparse in situ observations and a probabilistic regression framework. We call this post-processing approach Generalised Probabilistic Regression (GPR), and implement it using both generalised linear models and artificial neural networks (i.e., multilayer perceptrons). By testing the GPR post-processing approach across three Hindu Kush-Karakoram-Himalaya basins with varying hydro-meteorological characteristics and four experiments, which are representative of real-world scenarios, we find it performs consistently much better than both raw regional climate model output and deterministic bias correction methods for generalising daily precipitation post-processing to ungauged locations. We also find that GPR models are flexible and can be trained using data from a single region or multiple regions combined together, without major impacts on model performance. Additionally, we show that the GPR approach results in superior skill for post-processing entirely ungauged regions, by leveraging data from other regions, as well as ungauged high-elevation ranges. This suggests that GPR models have potential for extending post-processing of daily precipitation to ungauged areas of HKH. Whilst multilayer perceptrons yield marginally improved results overall, generalised linear models are a robust choice particularly for data-scarce scenarios, i.e., post-processing extreme precipitation events and generalising to completely ungauged regions.