4.1.1. Shortlisting of Models: Changes in Climatic Means

The results of the initial shortlisting of the GCM model runs are given in Figures 3 and 4. In this step, only those GCM runs were retained which showed minimal difference with the 10th, 50th and 90th percentile values of Δ*T* ( ◦C) and Δ*P* (%), so that, for each RCP, we were left with sets of 4 GCM runs at each corner and 4 in the middle, while the remaining model runs were not processed any further. In this way, a total of 20 model runs were selected for each RCP.

It is worth mentioning that the range of projections for Δ*T* and Δ*P* for the RCP8.5 model pool was much larger than for the RCP4.5 model pool. For the latter, more extreme RCP, Δ*P* ranges from −5.42% to 19.56%, and Δ*T* ranges from 1.26 ◦C to 5.41 ◦C; while for the former (RCP4.5), these ranges are much higher, with Δ*P* ranging between −12.01% and 35.12% and Δ*T* between 1.48 ◦C and 8.57 ◦C.

The shortlisted GCM runs were also ranked according to their differences with the 10th, 50th or 90th percentile values in the respective corner or center. This ranking was intended for use in the final selection step, so that those model runs which show closest representation of the group of models or type of scenarios (Warm-Wet, Warm-Dry, Cold-Wet, Cold-Dry or the Median) get preference during the final selection.

It should be noted that the term Cold used in the "Wet-Cold" and "Dry-Cold" scenarios does not mean that the future temperatures will be colder than those of the reference period, but rather indicates that the warming will be less than that of the Warm scenarios. Similarly, the term Dry in the scenarios "Dry-Cold"and "Dry-Warm" is also only indicative of its comparative position relative to other climate models.

**Figure 3.** Projected changes in mean air temperature (Δ*T)* and annual precipitation sum (Δ*P)* between 2071 and 2100 and 1971 and 2000 for all included RCP4.5 GCM runs. Blue crosses indicate the 10th, 50th and 90th percentile values for Δ*T* and Δ*P*. The model runs shortlisted during this step are indicated in red color.

**Figure 4.** Similar to Figure 3, but for RCP8.5 GCM runs.

#### 4.1.2. Ranking Based on Changes in Climatic Extremes

The 20 shortlisted model runs for each RCP were further scrutinized based on their projected changes in climatic extremes. The details of the projected changes in selected extreme indices are given in Table 3. The darker colors indicate the higher values, while the lighter indicates lower values. These indices were given a weighted rank/score based on their difference from the highest value in the group of four model runs in a corner.

Similar to the rank assigned based on changes in the means, this ranking was also intended for use in the final selection step, so that the model runs, which show the largest changes in the extreme indices for each of the corner: Warm-Wet, Warm-Dry, Cold-Wet or Cold-Dry, get preference during the final selection. Unlike the four corners, evaluation based on the extreme indices was not carried out for the central or the mean scenario.

The ranking and scores for means and extreme indices, as well as the skill scores for simulating reference climate, are presented in Tables 4 and 5 for RCP4.5 and RCP8.5, respectively.

In most cases, the model run with the highest or the lowest changes in mean precipitation or temperature coincide with the highest change in relevant extreme index as well.

The index Δ R99pTOT (%) was evaluated to represent the *"*Wet" scenarios, while the Δ CDD (%) represented the "Dry" scenarios. Similarly, Δ WSDI (%) was considered for the "Warm" scenarios, while Δ CSDI (%) was considered for the "Cold" scenarios. In this way, a set of two (2) indices out of the four (4) were evaluated for each of the scenarios: Warm-Wet, Warm-Dry, Cold-Wet, and Cold-Dry.


**Table 3.** Percentage change in ETCCDI indices (R99pTOT, CDD, WSDI, and CSDI) along with changes in mean precipitation (Δ*P)* and temperature (Δ*T)*, for all corners/scenarios (Warm-Wet, Warm-Dry, Cold-Wet and Cold-Dry) and both RCPs (RCP4.5 and RCP8.5).

4.1.3. Ranking Based on Skill in Reproducing the Reference Climate

After checking the model runs for their projected changes in means and extreme indices, they were finally evaluated for their skill at reproducing the reference precipitation and temperature data.

The ranking for past performance utilized a new set of reference precipitation and temperature data [7], averaged over the UIB. The skill scores were calculated following the procedure of Section 4.1.3, and are presented in columns g and h in Tables 4 and 5. For most scenarios, the same models performed better than the others for both RCPs in simulating past climate.

*Climate* **2018**, *6*, 89

After allocating the skill score based on the past performance, the final skill scores and ranks were calculated by multiplying all the relevant skill scores allocated to each model run. The final ranks were allocated to each scenario, with the highest rank allotted to the model run with highest final skill score, and so on.

It is interesting to note that for the 4 scenarios, Warm-Dry, Cold-Wet, Cold-Dry and Median, for both RCPs, the same GCMs get the highest skill scores and ranks. The only exception is the Warm-Wet scenario, where different models top the ranking. In this scenario, for RCP4.5, the GCM "MIROC5" is in the top rank, followed by "CanESM2", while for RCP8.5, the ranking of these two GCMs is reversed.

**Table 4.** Weighted ranks for all shortlisted RCP4.5 GCM runs based on change in means (e and f), change in extremes (a, b, c and d) and their skill scores for simulating reference precipitation and air temperature(g and h).



**Table 5.** Similar to Table 4, but for RCP8.5.
