The Hindu-Kush Karakoram Himalaya (HKKH) mountains and the Tibetan plateau are the world's largest snow and ice reservoir outside the polar regions and they are often referred to as the "Third Pole". These mountains feed the most important Asian river systems, and changes in snow and precipitation dynamics in this area could severely impact on water availability for downstream populations, agriculture and energy production, ecosystems and biodiversity. Despite their importance, precipitation and snowpack characteristics in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high elevation area. Global Climate Models (GCMs) still have too coarse spatial resolution to reproduce the small scale variability of precipitation and snow in orographically complex areas. Nevertheless, they may be effective in providing, even at a regional scale, a smooth but coherent picture of the large scale temporal and spatial patterns of these two variables in these areas. The quantification of the uncertainties in GCM simulations is essential to define the models skills in reproducing climate variability and to critically analyze future climate change projections. We investigate how the spatial and temporal variability of precipitation and snowpack in the HKKH region is represented in historical and future simulations of the state-of-the-art GCMs participating in the CMIP5 effort, and we investigate the role of elevation-dependent surface warming. The model outputs in the historical period are compared with the main, currently available observational datasets, including surface- and satellite-based observations and reanalysis data.