*4.4. Limitations of the Downscaling and Bias Correction Approach Adopted*

The downscaling and bias correction approaches described in the previous sections, although adopted as the best available option considering the availability of time, resources and data, are still not without limitations.

Firstly, we opted for using GCM-RCM chains for the already shortlisted climate model runs where the RCM add further detail to global climate simulations and may provide regional- to local-scale information, but their outputs are still subject to inherited or new systematic errors and may therefore require a bias correction or further downscaling to a higher resolution, along with the fact that they may also produce quite a different response in terms of the temperature and precipitation change than the forcing GCMs. In fact, many authors recognized this affliction of GCM-RCM model chains with systematic errors (biases) [48–55], but still the representation of explicit atmospheric and surface processes and the level of spatial details provided by the RCMs [56] make them a better option for regions with high topographic variability, The selection procedure may in future, though, directly include the RCM runs instead of GCM, when an appropriate amount of RCM runs are available.

The systematic errors (biases) in GCM/RCM outputs make them unsuitable for certain uses, and application of different bias-correction methods has increasingly become a standard procedure, especially in climate change impact studies [57].

Although these bias correction approaches improve the agreement of climate model output with observations, and therefore narrows the uncertainty range of predictions and simulations, they do so without a sound physical basis [57]. At the same time, a growing number of authors are showing reservations on the use of bias correction methods for a variety of reasons. Some argue that the main assumption behind bias correction approaches is the stationarity of the correction parameters, which is not realistic and may not be the case, especially under climate change [57,58]. While others believe that as bias correction cannot overcome major climate model errors, inexperienced application might result in ill-informed adaptation decisions [59]. Despite being critical of bias correction methods, many of these authors e.g., [57,59,60] acknowledge the need for some type of bias correction and recommend different precautions to avoid any pitfalls.

#### **5. Conclusions**

It is essential to have representative future climate projections of appropriate quality for climate change impact studies, especially in the water resource sector. Despite the availability of an increasing number of GCM outputs in the CMIP5 archive and the on-going improvements in their process representations, issues of large uncertainties in their future climate predictions cannot be avoided. This situation, along with other factors, such as time, human resources or computational constraints, make it imperative to sort out the most appropriate individual GCM or small ensemble of GCMs for a more reliable assessment of climate change impacts.

The approach presented in the present study seeks the most suitable set of climate model runs, while considering not only the full ranges of projected changes in terms of means and extremes by different climate models, but also their skills in simulating the past climate in a reference period.

This selection procedure was applied for future climate projections over the Upper Indus Basin for two representative concentration pathways (RCPs), the RCP 4.5 and RCP 8.5. All available model runs for the r1p1i1 ensemble member of each GCM in the CMIP5 repository were included in the initial list. The total number of model runs available for RCP4.5 was 42, and 39 for RCP8.5.

Based on the huge uncertainties reported in the GCM runs for the UIB, all possible extreme future scenarios (Wet-Warm, Wet-Cold, Dry-Warm, Dry-Cold) were considered, in addition to the selection of GCMs representing the mean future climate change, with respect to both changes in the projected means and the extremes. This procedure made it possible to arrive at a limited number of climate models, from which the final selection was performed by assigning ranks based on the weighted score for each of the mentioned selection criteria.

Finally, the precipitation and temperature time series of the selected GCM model runs were bias corrected and further downscaled to the scale of the reference data by means of a distribution mapping technique. The ensembles of the selected GCM runs for RCP4.5 and RCP8.5 scenarios show that the uncertainty of future climate in the study region is very large for the raw data, as well as their downscaled versions.

The downscaled projections indicate increases of temperature ranging between 2.3 ◦C and 9.0 ◦C and changes in precipitation that range from a slight annual increase of 2.2% under the drier scenarios, to as high as 15.9% for the wet scenarios. Thus, for both temperature and precipitation, the future projections under all scenarios and both RCP's only show increases in the mean annual values, with no negative trend. Moreover, for all scenarios, the future precipitation is projected to be more extreme, as the probability of wet days decreases, while at the same time, the precipitation intensities will increase.

The spatial distribution of the downscaled predictors, namely, the precipitation, also shows distinct patterns across the UIB, such that this variable shows for all time periods/scenarios considered a distinct decrease in the southeastern parts, but an increase in the northeastern parts of the basin. This decrease/increase is particularly intense for the "Dry-Warm" and the "Median" scenarios over the late 21st century.

Overall, the future climate of the UIB region remains very uncertain, which justifies the selection procedure proposed here to arrive at a wider range of possible climate scenarios that can then be further utilized and translated into a wider spectrum of climate change impact scenarios.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2225-1154/6/4/89/s1.

**Author Contributions:** Conceptualization, A.J.K.; Formal analysis, A.J.K.; Investigation, A.J.K.; Methodology, A.J.K.; Resources, M.K.; Supervision, M.K.; Validation, A.J.K.; Writing—original draft, A.J.K.; Writing—review & editing, M.K.

**Funding:** This research received no external funding.

**Acknowledgments:** We acknowledge provision of data by the following sources:


**Conflicts of Interest:** The authors declare no conflict of interest.
