**1. Introduction**

Future climate projections provided by general circulation models (GCMs) can serve as the basic input for climate change impact studies on water resources. As the outputs from these general circulation models (GCMs) have only coarse spatial resolution, and so are often not suitable as direct input to distributed or semi-distributed hydrologic models, they have to be downscaled in most cases to appropriate (higher) resolutions. Such a downscaling can be done either through applying statistical

downscaling or through dynamical downscaling via use of a regional climate model (RCM) embedded in a larger GCM.

Despite the availability of a large number of GCM outputs in the CMIP5 archive, and the on-going improvements in their process representations, issues of large uncertainties with regard to the future climate are not yet avoidable. The inherent uncertainties, along with other factors such as time limitations, human resource availability, or computational constraints, make it imperative to sort out the most appropriate individual GCM or small ensemble of GCMs suitable for downscaling and subsequent use in the assessment of climate change impacts.

This aforementioned selection of GCMs is not simple or straightforward, as there can be nearly an unlimited number of criteria and approaches through which climate models can be evaluated for their skill and suitability for specific purposes and regions. In most cases, though, the selection can be based either on a single criterion or a whole set of criteria. One approach may be to consider the total change projected by the GCMs, in the means and/or extremes of a climate variable and its location on the overall spectrum of the future projected by all GCMs. Another approach may place more emphasis on the success of GCMs in simulating past climate for either the means, extremes, or seasonality [1,2] of the study region. Additionally, there may be approaches based on some combination of the aforementioned approaches. The first approach, which considers all the possible projected futures (stretching from warm and wet to cold and dry, or opting for the middle path of all possible futures) is becoming more relevant, especially in regions such as the Hindu Kush Himalayas (HKH) and UIB, where GCMs/RCMs have been reported to struggle in simulating the past climate [3–6]. As no individual model can be separated out as superior in simulating the past climate in the HKH region, it is therefore important to consider the full range of possible projected futures when focusing on assessments of climate change impacts.

The criteria to be used for selecting the most appropriate model runs are also defined based on their intended purpose or the region. Both of these factors are important, as a different intended uses my require consideration of assessment based on totally different skills or variables, while the importance of a specific selection criteria may differ for different locations and topographically contrasting areas. Additionally, as not all the available models may be equally good for specific locations, regions or topographies, the need for the assessment of the ability of climate models to reproduce important processes in the study region is vital and essential.

In the current study, we consider a combination of these approaches to shortlist climate model runs, along with utilizing new and improved data for the past climate in the UIB [7] for assessment of model skill in simulating the seasonal cycles in the region. The main aim of the study was to select a set of GCM simulations that can represent the full spectrum of the future climate, as projected by the entire pool of climate models, in term of both means and extremes, and which can be subsequently used as climate forcing for hydrological modelling to assess a wider range of possible climate change hydrological impacts, especially for the expected changes in water yield, annual cycle, high and low flows, and floods.

The specific objectives of the study included:

