*3.1. Selection and Shortlisting of GCMs/RCMs*

The full spectrum of GCM projections is wide, with large uncertainties attached [35–37], and it cascades to even a larger spectrum when downscaled or translated into possible impacts. Furthermore, the available future projections differ vastly from each other and may range from very wet to drier or very warm to colder future climates, so that the models can be categorized as representing either

Warm-Wet, Warm-Dry, Cold-Wet and Cold-Dry corners of the full spectrum, in addition to the projections which are around the median tendency of future model projections.

**Figure 2.** Reference climate data: (**a**) Mean annual precipitation (mm), (**b**) mean temperature- maximum ( ◦C), and (**c**) mean temperature- minimum (◦C).

These issues have led to diverse views on how to select or use these climate model projections, or even whether these climate models or their downscaled outputs should explicitly be used at all, or should only be indirectly used, instead, as guides to generate a range of plausible scenarios more suited for targeted impact studies and practical adaptation planning [38].

In mountainous regions, such as the Upper Indus Basin (UIB), the issue of how to proceed with the climate change impact studies becomes more complicated, because not only may the uncertainties shown by the climate models for these regions be even greater [4,39], but also because of the lower margin for error, as the lives and livelihood of millions of people depend purely on the water resources generated in these basins.

The usual approach of selecting results of a certain model or group of models or opting for a scenario with the mean trend of future projections may not be practical, as the full range of possible future climatic conditions needs to be covered in order to assess the full range of expected impacts required for climate adaptation needs.

As mentioned earlier, the selection of GCMs can be done following different approaches and may be based on a single criterion or a set of criteria. These approaches may include criteria such as: the total amount of change in the mean and/or an extreme of a projected climate variable; the success of a GCM in simulating the past climate for means or extremes; or maybe the skill in presenting the same pattern of tele-connections that drive the climate of the study region, and so on.

The current study adopted a combination of some of these approaches and applied a step-wise shortlisting of climate models based on a range of projected change in the (a) mean, (b) extremes, and (c) skill in reproducing the past climate. As the aim was to arrive at a limited number of models that can represent not only all the possible futures as projected by the entire pool of climate models, but also changes in climatic extremes, so that the selected model runs can provide representation of the full spectrum of future climate projections by GCMs in terms of change in mean, as well as extremes. In other words, for each selected RCP, we intended to filter and select five climate model runs, each representing one the four corners of the spectrum or the median tendencies.
