4.1.4. Limitations of the Model Selection Procedure

In the previous section, the step-wise shortlisting of the various climate models was based on the range of projected change in (a) mean, (b) extremes, and (c) skill in reproducing the past climate. Although the main aim of this approach was to combine the strengths of two different methodologies, i.e., the selection of the GCMs based on the properties of the full range of projections and the selection procedures based on past performance, certain limitations are unavoidable and need to be discussed.

First of all, the analysis considered only models selected based on the changes in the means and only the ensemble member *r1p1i1*, resulting in a reduced number of GCM runs for evaluation and possibly a smaller range of climatic extremes. This may also have led to possibly screening out models which may have had better past performance.

Similarly, another issue is concerned with the scale at which the method was applied. During the shortlisting step, and also during the evaluation of extreme indices, the projected changes or ETCCDI extremes indices were averaged over the entire UIB, which has the possibility of decreasing the spatial variation in projected changes.

Additionally, the weighting of different skill scores in this study also differed for similar work, such as [21]. In our study, the final skill score was a combination of scores allocated for change in mean, change in extremes, and performance in reproducing past climate. This may have reduced the chances of selecting the climate model with the best past performance, but increased the chance of a better spread of scenarios over the entire range, while still taking past performance as a key factor in selections. Our model selection approach also assumes that all the evaluated model runs are independent of each other, which may not be the case, as some models use the same forcing and validation data or may share similar model codes [44,45].

Despite these limitations, the adopted approach made it possible for us to identify a limited number of model runs, representative of the full range of future projected means and extremes while giving due preference to models which perform better at simulating the reference climate.
