GLOFRIS model cascade inundation downscaling technique, which is described in detail in Winsemius et al. (2013) and applied at global scale in Ward et al. (2013) and Jongman et al. (2015). This method uses the global hydrological model PCR‐GLOBWB‐DynRout (Van Beek and Bierkens, 2009; Van Beek et al., 2011) to simulate a set of maps showing inundation extent and depth for each grid cell and for all return periods. Three limitations are important to acknowledge at this point. First, we assume that there are no flood protection standards in place. Even though data on protection standards are available for a number of high‐income countries (Jongman et al., 2014), a global database of flood protection standards does not exist. However, as protection standards are related to a country’s wealth, it is likely that protection standards across the developing countries investigated are relatively low. Second, coastal floods and storm surges are not considered in the analysis (as the model is for river floods only), even though they can have large consequences (Hallegatte et al., 2013). And third, smaller rivers, and pluvial and flash flooding are not represented in the global inundation modelling at its current scale.

# **3.1.2. Drought hazard**

While defining flood conditions may be straightforward (e.g. inundation depth per grid cell per return period), there are many possible definitions of a drought. According to IPCC (2012a) meteorological droughts refer to deficits of precipitation; hydrological droughts refer to negative anomalies in water availability from surface water or groundwater, regardless of human demand; and socio‐economic droughts include demand to calculate a water deficit. Each of these drought phenomena can also be defined over different timescales, which are relevant for different issues (e.g. damages to buildings vs. agricultural losses). Here, we applied a variable monthly threshold method (namely the 80% exceedance probability of discharge, _Q80_ ) to estimate the yearly maximum cumulative discharge deficit (with respect to the _Q80_ threshold) per grid cell at 0.5° resolution as a measure of hydrological drought (Lehner and Döll, 2001; Wada et al., 2013; Wanders and Wada, 2014), using outputs from the PCR‐GLOBWB model Van Beek et al., 2011). A cumulative discharge deficit is the accumulated amount of discharge under the Q80 threshold over a continuous period of time.

The resulting maps express the relative intensity of drought conditions to long term mean stream flow conditions and can be interpreted as the amount of time a long‐term mean discharge would be needed to overcome the maximum accumulated deficit volume occurring with a certain return period. We assumed that hazardous conditions will occur when this value exceeds 3 months, moreover, we tested the robustness of our results using a 1‐month and 6‐month threshold. The indicator does not include information on groundwater availability or upstream water use. Furthermore, this indicator does not account for the spatial variability in water needs. The resulting drought values should therefore be interpreted as conservative (underestimating drought hazard), although for an assessment of the actual water scarcity conditions one should include local water needs, upstream water uses, water transfers, and natural or man‐made water storages (Wada et al., 2013).

# **3.1.3. Future flood and drought hazard**

Further, the models are used to estimate future climate change impacts on flood and drought hazard, for different time periods (1960‐1999, 2010‐2049, 2030‐2069, and 2060‐2099), using meteorological outputs from GCMs, forced by two representative concentration pathways (RCPs, Van Vuuren et al., 2011), which represent scenarios of future concentrations of greenhouse gases (RCP 2.6 and 8.5), and five global climate models (GCMs). Since the GCMs used contain bias due to unrepresented intra‐ 6