**Acknowledgements**

However, when individual evolutions of PDSI values for each numerical experiment are analyzed, the intermodel and internal variability show up revealing cases in which the climaterelated signal of drought tendencies is not always present as for the ensemble mean. In **Table 3**, the PDSI changes for the interval 1970–2100 are presented for each numerical experiment listed in **Table 1** and for each river basin. This table illustrates the uncertainty associated with the signal revealed by the ensemble mean. The level of uncertainty related to the summer drought signal seems to be lowest for the Arges river basin if we count, from **Table 3**, the number of experiments for which there are higher PDSI changes in magnitude under the RCP 8.5 than those under the RCP 4.5 (four from five experiments). In this context, the level of

Data are from multimodel means of a five-member ensemble consisting of five numerical

The regional modeling approach we have proposed here provides a base for exploiting simple models such as Palmer water balance in a physical consistent manner: (1) without the need to apply bias corrections and (2) reducing uncertainty by eliminating additional sources of errors which are brought through coupling RCMs with complex hydrological models. Of course, these advantages come with a cost: information provided by our proposed methodology is basin-averaged and cannot account for details at the sub-basin level

Our methodological approach is based on two pillars: the Palmer water balance model validated and applied at catchment level and the multimodel ensemble of regional climate experiments (which provides physically consistent information under climate change scenarios). The RCM ensemble analyzed in this chapter manages to reproduce relatively well the observed components of water cycle such as potential evapotranspiration and precipitation when multimodel averages of regional climate results are used over the river basins. These results provide a certain level of confidence when analyzing future evolution of precipitation, potential evapotranspiration, potential runoff and related indices in the area of interests

The correlations between observed streamflow at the observation stations in each basin and PDSI-related indices show that the PDSI represents reasonably well the local water balance and drought-related processes taking place in the catchments (**Figure 3**). This allows us to use the basin-averaged PDSI computed with multimodel ensemble data for the assessment of the climate change impact on drought over the selected basins under the moderate and worst-case concentration scenarios. Spatial average procedure applied here to gridded PDSI provides robust results.

uncertainty is highest for the Somes river basin (one from five experiments).

Data are from the five numerical experiments (see **Table 1**).

which could be essential for some specific application.

experiments (see **Table 1**).

126 Engineering and Mathematical Topics in Rainfall

under climate change scenarios.

**5. Conclusions**

This chapter was supported by a grant of the Romanian National Authority for Scientific Research and Innovation, CCCDI—UEFISCDI, project number 81/2016 within PNCDI III"("Improving Drought and Flood Early Warning, Forecasting and Mitigation using real-time hydroclimatic indicators"—IMDROFLOOD) and by the European Commission financed under the ERA-NET Cofund WaterWorks2014 Call. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI). The description of Palmer Drought Severity Index presented in the first part of the Methodology section is taken from our work published in the reference [2]. We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modeling, former coordinating body of CORDEX and responsible panel for CMIP5. We thank the climate modeling groups mentioned in **Table 1** for producing and making available their model output. Also, we acknowledge the Earth System Grid Federation infrastructure, an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modeling and the partners in the Global Organization for Earth System Science Portals (GO-ESSP).
