Climate change impact on flood and extreme precipitation increases with water availability — TEI-tagged view

Hossein TabaripersName (2020date). “Climate change impact on flood and extreme precipitation increases with water availability.” Scientific Reportstitle·j.

Object of study — projected extreme precipitationterm·dom and flood intensityterm·dom changes against spatial and seasonal water availabilityterm·dom

Research question — how changes in extreme precipitationterm·dom and flood intensityterm·dom relate to spatial and seasonal water availabilityterm·dom

Contribution — attention should go to how much water is available, not only how much the atmosphere can hold

Full text — tagged

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Climate change impact on flood and extreme precipitationterm·dom increases with water availabilityterm·dom Abstract The hydrological cycle is expected to intensify with global warmingterm·dom, which likely increases the intensity of extreme precipitationterm·dom events and the risk of flooding. The changes, however, often differ from the theorized expectation of increases in water‐holding capacity of the atmosphere in the warmer conditions, especially when water availabilityterm·dom is limited. Here, the relationships of changes in extreme precipitationterm·dom and flood intensities for the end of the twenty-first century with spatial and seasonal water availabilityterm·dom are quantified. Results show an intensification of extreme precipitationterm·dom and flood events over all climate regions which increases as water availabilityterm·dom increases from wet to dry regions. Similarly, there is an increase in the intensification of extreme precipitationterm·dom and flood with the seasonal cycle of water availabilityterm·dom. The connection between extreme precipitationterm·dom and flood intensityterm·dom changes and spatial and seasonal water availabilityterm·dom becomes stronger as events become less extreme.

Extreme precipitationterm·dom is expected to intensify with global warmingterm·dom over large parts of the globe as the concentration of atmospheric water vapour which supplies the water for precipitation increases in proportion to the saturation concentrations at a rate of about 6–7% per degree rise in temperature according to the thermodynamic Clausius–Clapeyron relationshipterm·dom 1 – 3 . However, changes in atmospheric dynamics such as poleward expansion of tropical Hadley circulation can weaken 4 – 6 or reinforce 7 the thermodynamic effect regionally and modify the extreme precipitationterm·dom amplification. Water availabilityterm·dom also plays a large role in the moisture–temperature relationship 8 .

Due to different interacting drivers of extreme precipitationterm·dom changes, the changes are not uniform in space and vary by region 9 . The scaling rate of extreme precipitationterm·dom with land surface temperature is not accordingly constant. Even a negative scaling at higher temperatures has been observed in some places 10 , which has been suggested to be a result of limited moisture availability 11 or arid surface conditions 12 . Whereas in wet regions amplified atmospheric moisture convergence can intensify the effects of extreme precipitationterm·dom, in dry environments precipitation increases may be counteracted by evaporation 13 . Recent studies have examined daily extreme precipitationterm·dom changes in relation to water availabilityterm·dom and found that 30-year averaged annual precipitation maxima aggregated over the dry and wet regions of the world is likely to increase 14 , 15 . As rarer precipitation events are expected to be more influenced by climate change 7 , 16 , 17 and scale with vertical moisture transport rather than horizontal moisture advection 6 , it remains unresolved whether the relationships between extreme precipitationterm·dom changes and water availabilityterm·dom can also be detectable for rare flood-producing precipitation events.

Using gridded observations in Europe, a positive scaling rate of extreme precipitationterm·dom with temperature in winter and a negative one in summer has been reported 18 . This raises the question of whether extreme precipitationterm·dom changes have any relation with the seasonal cycle of water availabilityterm·dom in a similar fashion as regional water availabilityterm·dom. Understanding of the relationships between the climate change impact on extreme events and water availabilityterm·dom is essential in the future-proofed planning for global change in different climate regimesterm·key to ensure a sustainable socioeconomic development at the regional scales.

Extreme precipitationterm·dom amplification may increase the intensity and frequency of flooding, imposing heavy costs to aquatic and terrestrial ecosystems, human societies and the economy. Changes in flood characteristics not only depend on the spatial distribution, time evolution and rarity of precipitation 19 , but on antecedent soil moisture conditions and in snow-dominated regions on snowmelt timing 20 and snowpack magnitude 21 . Global-scale flood assessments have reported both decreases and increases in future floods under global warmingterm·dom 22 – 25 , albeit by using varying hydrological and climate models, scenarios, bias-correction methods and flood indicators which hinders drawing a common perspective of future flood changes 26 . Changes in soil moisture and runoff have been shown to correlate well with changes in climatic moisture at the regional scale 27 , 28 . More significant changes have also been found in observed annual maximum flows in wet regions than in dry regions 29 . This is because in the former a higher fraction of precipitation changes leads to runoff changes, while in the latter a large buffer is available to dampen precipitation changes causing smaller runoff changes 30 – 32 . Owing to the complex mechanisms of flood changes, it is not known whether relationships between extreme precipitationterm·dom changes and water availabilityterm·dom can be generalized for flood changes.

Here, the relationships of future changes in extreme precipitationterm·dom and flood intensities with water availabilityterm·dom are analyzed. Extreme precipitationterm·dom changes per K global warmingterm·dom in 2070–2099 under the RCP8.5term·key scenario compared with 1971–2000 are computed using simulationsterm·mth of 24 global climate modelsterm·dom (GCMsterm·dom) from the Coupled Model Intercomparison Project Phase 5term (CMIP5term 33 ) and flood changes using simulationsterm·mth from multi-model ensemble of five global impact modelsterm·dom (IMsterm·dom) and four CMIP5term GCMsterm·dom (20 IM-GCM combinations) from the Inter-Sectoral Impact Model Intercomparison Projectterm (ISIMIPterm 34 ). The climatological water availabilityterm·dom is determined based on the aridity indexterm·dom (AIterm·dom), as the ratio between potential evapotranspirationterm·dom (water demand) and precipitation (water supply), for historical and future simulationsterm·mth of individual CMIP5term models (see “ Methods ” for details). It ensures that the expected changes in the geographical location of the climatic regions with global warmingterm·dom and the discrepancy among models are taken into account.

Results and discussion Relationships of flood and extreme precipitationterm·dom changes with spatial water availabilityterm·dom Based on the ensemble median of the CMIP5term GCMsterm·dom, water-limited regions are mainly located in North Africa & the Middle Eastterm (MENAterm) and Australia, while water-abundant regions are located in the mid-latitudes and the tropics (Fig. 1 a). About 72% of the global land is likely to undergo aridificationterm·dom in the future, with substantial aridificationterm·dom (aridity increase of > 30%) in MENAterm, south Europe, south Africa and Australia (Fig. S3 ), leading to a shift in climate regimesterm·key (Fig. 1 b). Globally, arid and semi-arid regions would expand by 10.3% and 9.9%, respectively, while humid and semi-humid regions would decrease by 2.3% and 4.9%, respectively (Fig. 1 c). It makes the area coverage of humid, semi-humid, semi-arid and arid climates at the end of the twenty-first century equal to 55%, 20%, 11% and 14% of the total terrestrial land area, respectively (Fig. 1 d). Figure 1 Aridity indexterm·dom and its expected future changes. ( a , b ) Spatial distribution of ensemble median aridity indexterm·dom and five climate regimesterm·key based on the aridity indexterm·dom of the ( a ) historical (1971–2000) and ( b ) future (2070–2099) climates. ( c ) Projected change in area coverage of each climate regimeterm·key for the period 2070–2099 with respect to the reference 1971–2000 (salmon dots, individual models); ensemble median is shown by black cross and number at the top. ( d ) Ensemble median projected area coverage of each of the five climate regimesterm·key in percent of total terrestrial land area for 2070–2099. The maps were generated using the MATLAB mapping toolbox 65 (URL https://www.mathworks.com/products/mapping.html ).

There is a consistency between most CMIP5term GCMsterm·dom on the geographical distribution of climate regimesterm·key (Figs. S4 and S5 ). IPSL models are the ones with the largest difference from the other models, which simulate much larger water-limited regions. Because of this discrepancy among GCMsterm·dom on the location of different climate regimesterm·key as well as the expected shift in the regimes in the future, the relationships of extreme precipitationterm·dom and flood changes with water availabilityterm·dom are investigated based on model-specific masks of dynamic (future) climate regimesterm·key.

The spatial distribution of changes in 1-in-30-year extreme precipitationterm·dom intensity shows that extreme precipitationterm·dom increases uniformly across all the climate regimesterm·key (Fig. 2 ), in agreement with previous findings for rare extreme events 35 , 36 . Precisely, 99.9%, 99.8%, 99.3% and 98.7% of land area respectively in humid, semi-humid, semi-arid and arid regions show an increase in extreme precipitationterm·dom intensity (Fig. 2 ). There is, nevertheless, a substantial uncertainty in the multi-model median changes in Latin America, Africa, the Middle East and Australia (Figs. S5 and S7 a). This is in line with the uncertainty hotspots of less extreme precipitationterm·dom identified in earlier studies 9 . A large uncertainty in the uncertainty hotspots has been attributed to the convective nature of rainstorms which cannot be adequately represented and resolved by coarse-scale GCMsterm·dom, and to a sparse observational network which hinders the tuning and improvement of GCMsterm·dom over these regions 9 . Figure 2 Changes (%) in 1-in-30-year extreme precipitationterm·dom intensity per K global warmingterm·dom in 2070–2099 under RCP8.5term·key, compared with 1971–2000. ( a – d ) Spatial distribution of ensemble median changes in ( a ) humid, ( b ) semi-humid, ( c ) semi-arid and ( d ) arid regions. ( e ) Changes per climate regimeterm·key based on individual models (salmon dots). For each violin, ensemble median is shown by black cross. The numbers on top of the violins (top row) indicate ensemble median and those in bold face and italic denote significant changes at the 95% and 90% confidence levels, respectively. The numbers in brackets indicate the percentage of experiments that agree on the sign of change (robustness). The maps were generated using the MATLAB mapping toolbox 65 (URL https://www.mathworks.com/products/mapping.html ).

As the particular focus of this study is to investigate extreme precipitationterm·dom and flood changes with water availabilityterm·dom, changes are analyzed with respect to global climate regimesterm·key. Extreme precipitationterm·dom in humid regions increases with global warmingterm·dom (6.31%/K) at the Clausius–Clapeyronterm·key rate (Fig. 2 e). The extreme precipitationterm·dom increase in humid regions is significant at the 5% level and robustterm·mth where the increasing signal is projected by 92% of the GCMsterm·dom. For semi-humid regions, the increase is smaller at the rate of 5.89%/K which is significant at the 5% level and robustterm·mth (88% of the GCMsterm·dom). Moving towards more water-limited regions, the extreme precipitation-temperature scaling rate further decreases; 5.62 and 5.45%/K for semi-arid and arid regions, respectively. The extreme precipitationterm·dom increase is significant and robustterm·mth for both semi-arid and arid regions, although with a lower level of robustness and significance. In the mid-to-high latitudes which occupy a large portion of global humid and semi-humid regions, extreme precipitationterm·dom changes are mainly controlled by the thermodynamic effect 4 – 6 . In low latitudes where most arid regions are located, the intensifying tendency of extreme precipitationterm·dom originating from thermodynamics is offset by the dynamic effectterm·dom 4 – 6 .

The total uncertainty in extreme precipitationterm·dom changes indeed depends on climatic regime and increases with decreasing water availabilityterm·dom (Fig. S8 a,c). Earlier studies have also found a larger spread of extreme precipitationterm·dom changes in dry regions compared wet regions 14 , 37 . The increasing total uncertainty with decreasing water availabilityterm·dom is attributed to the increasing trend of both its components (GCMterm·dom and hazard quantification method); however, the GCMterm·dom uncertainty has a much larger contribution in all regions.

Flood intensityterm·dom is projected to increase over most areas of the globe (Fig. 3 ), with a large uncertainty in some places (Figs. S7 b and S10 ). 75.9% of land area in humid regions shows an increase in flood intensityterm·dom, while semi-humid and semi-arid regions show a lower percentage of land area with increasing flood intensityterm·dom, accounting for 68.7% and 63.4%, respectively (Fig. 3 ). Flood changes follow the extreme precipitationterm·dom change direction over regions where precipitation plays the dominant role in flood occurrence; however, inconsistent changes are found where there are other flood generating factors in play (Fig. S10 ). Decrease in flood intensityterm·dom is observed in snow dominated regions (e.g., North and Central Europe) where spring snowmelt is decreasing under global warmingterm·dom 38 or in regions (e.g., Mediterranean) where annual precipitation is projected to decrease 39 , 40 and where antecedence soil moisture plays a significant role in flood generations 19 , 41 . Figure 3 Changes (%) in 1-in-30-year flood intensityterm·dom per K global warmingterm·dom in 2070–2099 under RCP8.5term·key, compared with 1971–2000. ( a – c ) Spatial distribution of ensemble median changes in ( a ) humid, ( b ) semi-humid and ( c ) semi-arid regions. ( d ) Changes per climate regimeterm·key based on individual experiments (salmon dots). For each violin, ensemble median is shown by black cross. The numbers on top of the violins (top row) indicate ensemble median and those in bold face and italic denote significant changes at the 95% and 90% confidence levels, respectively. The numbers in brackets indicate the percentage of experiments that agree on the sign of change (robustness). Grid cells with annual maxima close to 0 m 3 s −1 of the historical model period are screened out. The maps were generated using the MATLAB mapping toolbox 65 (URL https://www.mathworks.com/products/mapping.html ).

Aggregated over different climate regimesterm·key, broadly similar pattern to extreme precipitationterm·dom changes is obtained for flood changes where flood intensityterm·dom increases in all climate regimesterm·key with the magnitude increasing with water availabilityterm·dom (Fig. 3 d). As most of arid regions are masked out for the flood analysis, the relationship of flood changes with water availabilityterm·dom is examined by comparing the results among humid, semi-humid and semi-arid climate regimesterm·key. Flood intensityterm·dom increases at the rates of 5.07, 3.63 and 3.12%/K for humid, semi-humid and semi-arid climate regions, respectively. The increase for all regions is significant and robustterm·mth with an agreement among > 75% of the experiments. The uncertainty in projected flood intensityterm·dom changes increases as the climate gets drier (Fig. S8 b,d). It highlights the necessity of using large multi-model ensembles including multiple impact models forced by several climate models for hydrological climate change analyses in drier regions. For all climate regimesterm·key, GCMsterm·dom are the main contributor to the flood change uncertainty, while hazard quantification methods have the lowest contribution. IM uncertainty in humid regions is larger than global average IM uncertainty. The dominance of the GCMterm·dom uncertainty corroborates the findings of Hagemann et al. 42 and Giuntoli et al. 43 who showed a larger contribution of climate model uncertainty compared to global hydrological models.

The results also show that using the median ensemble mask of climate regimesterm·key instead of model-specific masks and assuming static climate regimesterm·key instead of dynamic ones would lead to bias in extreme precipitationterm·dom and flood intensityterm·dom changes for different climate regions (see Texts S3 and S4 and Figs. S11 and S12 for more details). The bias increases towards drier climates and is larger in the case of using the median ensemble mask of climate regimesterm·key compared to static climate regimesterm·key.

Relationships of flood and extreme precipitationterm·dom changes with the seasonal cycle of water availabilityterm·dom It is also of interest to understand how extreme precipitationterm·dom and flood changes would vary with the seasonal variation of water availabilityterm·dom. Because there is little land in the Southern Hemisphere mid-latitudes, the seasonal analysis is limited to the Northern Hemisphere mid-latitudes with a strong seasonal cycle of water availabilityterm·dom. Similar to the relationships with the spatially-varying water availabilityterm·dom over the globe, extreme precipitationterm·dom changes have a clear connection with seasonal water availabilityterm·dom (Fig. 4 a). During the wet season when there is a moisture surplus, extreme precipitationterm·dom increases get close to or exceeds the Clausius–Clapeyronterm·key rate, while the increase is smaller during the dry season. The extreme precipitationterm·dom significantly and robustly increases in DJF (December–January–February), SON (September–October–November), MAM (March–April–May) and JJA (June–July–August) at the rates of 7.26, 6.70, 5.98 and 4.95%/K, respectively. A similar seasonal water availabilityterm·dom dependence of changes is obtained for flood intensityterm·dom (Fig. 4 b). While small and insignificant flood intensityterm·dom increases of 0.89 and 1.16%/K respectively are seen for JJA and MAM, the increase gets as large as 5.90 and 9.53%/K in SON and DJF, respectively. Figure 4 Changes in 1-in-30-year ( a ) extreme precipitationterm·dom and ( b ) flood intensityterm·dom per season (salmon dots, individual models) in the Northern Hemisphere mid-latitudes. For each violin, ensemble median is shown by black cross. The numbers on top of the violins (top row) indicate ensemble median and those in bold face and italic denote significant changes at the 95% and 90% confidence levels, respectively. The numbers in brackets indicate the percentage of experiments that agree on the sign of change (robustness).

Response of precipitation and flood extremity to water availabilityterm·dom In order to explore the response of precipitation and flood extremity to the spatial and seasonal variations of water availabilityterm·dom, all the analyses are repeated for less extreme precipitationterm·dom and flood events with return periods ranging from 2 to 29 years. The less the extreme precipitationterm·dom, the stronger the relationship of the changes with water availabilityterm·dom (Fig. S13 ). The slope of changes with spatial water availabilityterm·dom (from humid to arid regions) decays faster with precipitation extremity than that with seasonal water availabilityterm·dom (from DJF to JJA). Similar to extreme precipitationterm·dom, a weaker change relationship with spatial water availabilityterm·dom is found for rarer flood events. The relationship of flood changes with seasonal water availabilityterm·dom is, however, independent of flood extremity.

Extreme precipitationterm·dom and flood changes in different climate regions converge for more extreme events, due to a faster increment of the increases with event extremity in drier climates (Fig. 5 ). The thermodynamic factors play the main role for more extreme precipitationterm·dom changes 44 , while for less extreme events dynamic factors are also responsible for regional precipitation changes which may weaken the thermodynamic effect 4 – 6 . In a similar manner, extreme precipitationterm·dom changes in different seasons converge for more extreme events, because of a faster increment of the increases with event extremity in drier seasons (Fig. 5 ). Figure 5 Intensity changes in ( a , c ) extreme precipitationterm·dom and ( b , d ) flood events with return periods ranging between 2 and 30 years per ( a , b ) climate regimeterm·key and ( c , d ) season based on multi-model ensemble median. The changes per seasons are computed for the Northern Hemisphere mid-latitudes.

In terms of the area coverage of increasing signals, while the percentage of wet land areas (humid and semi-humid regions) with an increasing intensity signal of extreme precipitationterm·dom remain almost constant when events become more extreme, the percentage area noticeably increases with event extremity in drier regions (e.g., from 51.9% for 1-in-2-year events to 98.7% for 1-in-30-year events in arid regions) (Fig. S14 ). Likewise, the percentage area of drier regions with an increasing flood intensityterm·dom signal rises larger with event extremity: 20% area increase from 1-in-2-year events to 1-in-30-year events in semi-arid regions versus 11% and 5% area increases in semi-humid and humid regions, respectively (Fig. S14 ). A similar pattern is found in the seasonal analysis where a larger increment of the land area of increasing signals with event extremity is seen in dry season compared to wet season: from 83% (71%) for 1-in-2-year extreme precipitationterm·dom to 96% (77%) for 1-in-30-year extreme precipitationterm·dom (flood) in DJF as opposed to from 73% (42%) for 1-in-2-year extreme precipitationterm·dom to 95% (56%) for 1-in-30-year extreme precipitationterm·dom (flood) in JJA (Fig. S15 ). For more extreme events, the changes in flood intensities better follow the pattern of the extreme precipitationterm·dom changes. This is because, for more extreme events, flood timing is more likely to correspond to rainfall timing, while for less extreme events it is more influenced by soil moisture timing 45 .

Conclusions The results of this study suggest that changes in flood and extreme precipitationterm·dom intensities in response to global warmingterm·dom are significant and robustterm·mth when aggregated over different climate regions. Regionalization of the changes decreases the large noise of extreme events at local scale, leading to more robustterm·mth results. The increase in extreme precipitationterm·dom and the expected decrease in total precipitation in dry regions 46 , 47 supports “it never rains, but it pours” pattern 48 in these regions. The results show a clear connection of the flood and extreme precipitationterm·dom changes with spatial and seasonal water availabilityterm·dom, pointing to a larger increase for the regions and seasons with higher water (moisture) availability. Limited climatological water availabilityterm·dom in dry environments may offset extreme precipitationterm·dom increases, while in water-abundance conditions amplified atmospheric moisture convergence can intensify the effects of extreme precipitationterm·dom 13 . This suggests that attention should be paid not only on how much water the atmosphere can hold, but on how much water is available in the first place.

The flood changes in this paper are computed using two hazard quantification methods based a multi-model ensemble of 20 members including four GCMsterm·dom and five IMsterm·dom. Although it covers some important sources of uncertainty especially possible underestimation of flood changes from a single hydrologic model 23 , there exist other uncertainty sources related to the choice of hydrological model parameters, bias-correction approaches and downscaling methods which may further expand the uncertainty range. Moreover, the subset of four GCMsterm·dom used as the climate forcing in the ISIMIPterm IMsterm·dom may underestimate the full uncertainty in extreme precipitationterm·dom projections from the CMIP5term ensemble (Fig. S16 ). The uncertainty associated with the hazard quantification methodology, less quantified in previous studies, is particularly important in arid regions and needs to be included in future climate change assessments on extreme events.

Methods Data overview Daily precipitation simulationsterm·mth from 24 Coupled Model Intercomparison Project Phase 5term (CMIP5term 33 ) GCMsterm·dom for the historical period 1971–2000 and the future period 2070–2099 forced by Representative Concentration Pathway (RCP) RCP8.5term·key scenario are utilized: ACCESS1-0, ACCESS1-3, CanESM2, CMCC-CESM, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, HadGEM2-AO, HadGEM2-CC, Had-GEM2-ES, INMCM4, IPSL-CM5A-LR, IPSL-CM5AMR, IPSL-CM5B-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3. The first initial condition run (r1) of each CMIP5term GCMterm·dom is considered to ensure equal weighting of all models in a multi-model ensemble median. Monthly maximum, mean and minimum temperature data for the historical and RCP8.5term·key simulationsterm·mth from the same GCMsterm·dom are also used for climate classification and global warmingterm·dom estimation. The selection of the CMIP5term GCMsterm·dom is made based on the availability of daily precipitation and monthly temperature data for historical and RCP8.5term·key simulationsterm·mth. The simulationsterm·mth of the 24 CMIP5term GCMsterm·dom used in this study are of varying spatial resolutions (0.75°–3.75°). They are therefore resampled to a common 0.5° × 0.5° grid to match the spatial resolution of the discharge data. In order to minimize errors in the calculation of extreme precipitationterm·dom changes 49 , the changes are first computed on native model grids and then are interpolated to the common grid using the bilinear interpolation method. It was shown that the resampling results are not sensitive to the choice of the interpolation method and of the common grid size 9 . The extreme precipitationterm·dom analyses are restricted to all land grid cells where the impact of changes is predominantly felt.

In the Inter-Sectoral Impact Model Intercomparison Projectterm (ISIMIPterm 34 ) framework, four of the CMIP5term GCMsterm·dom (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) were downscaled by a trend-preserving method 50 and used as the climatic forcing to simulate daily discharge. Global daily discharge simulationsterm·mth from five impact models (IMsterm·dom) including three global hydrological models (H08 51 , MATSIRO 52 and WaterGAP2 53 ), one global land surface model (CLM4.5 54 ) and one dynamic global vegetation model (LPJmL 55 ) with 0.5° spatial resolution for the historical period 1971–2000 and the future period 2070–2099 under RCP8.5term·key scenario are used. All IMsterm·dom were setup using the same soil, land cover and morphologic data such that discrepancies among them is only due to their different process representations. The simulationsterm·mth with a varying land use and other human influences over the historical period (“histsoc” experiment) and then fixed at 2005 levels for the future period are used for all the IMsterm·dom except CLM4.5 which uses fixed year-2005 socioeconomic conditions (“2005soc” experiment) for both historical and future periods. The preliminary analysis shows that the influence of socioeconomic scenarios on flood intensityterm·dom changes is minor (Fig. S1 ). Grid cells with annual maxima close to 0 m 3 s −1 of the historical period are screened out for the further analysis, due to insufficient data for distribution fitting and smaller importance for flood analysis. The high latitude area in the Southern Hemisphere (> 60°S) which it is almost uninhabited and not subject to flooding is also excluded.

Quantification of climate change signals A 30-year return levelterm·mth of precipitation and river flow (average occurrence of once every 30 years) at each grid cell is used as the indicator of extreme precipitationterm·dom and flooding. To this end, annual maxima time series of extreme precipitationterm·dom and flow for each grid cell are derived for both historical and future periods. The generalized extreme value distributionterm·mth (GEVterm·mth) is fitted to the annual maxima series on the native grids of each model. The GEVterm·mth distribution is characterized by three parameters including location (µ; describing the center of distribution), scale (σ; describing the deviation around the mean) and shape ( ξ ; describing the tail behavior of the distribution) of the distribution. According to the shape parameter, three extreme distributions are defined as Fréchet, Gumbel and Weilbull corresponding to ξ > 0, ξ = 0 and ξ < 0, respectively. To quantify the uncertainty associated to the hazard quantification method, the extreme precipitationterm·dom and flood hazards are also determined using the peak-over-threshold (POTterm·mth) method with a Generalized Pareto Distributionterm·mth (GPDterm·mth). The GPDterm·mth distribution is also characterized by the location, scale and shape parameters and it leads to Pareto, Exponential and Beta distributions for ξ > 0, ξ = 0 and ξ < 0, respectively. The GEVterm·mth and GPDterm·mth parameters are estimated using the maximum likelihood method.

Changes in extreme precipitationterm·dom and flood intensities are defined as the ratio between the intensities of the end twenty-first century (2070–2099) and the end twenty century (1971–2000). To accounts for the effect of different climate sensitivities of the CMIP5term GCMsterm·dom, extreme precipitationterm·dom and flood changes of individual GCMsterm·dom are scaled by their changes in global average surface air temperature (Fig. S2 ) to derive units of %/K. Change in global average surface air temperature is calculated by comparing the 30-year global average annual temperature between the historical period 1971–2000 and the future period 2070–2099 under RCP8.5term·key scenario.

Association of changes with water availabilityterm·dom To investigate the relationships of the changes in extreme precipitationterm·dom and flood intensities with climatological water availabilityterm·dom, the climatological water availabilityterm·dom of each grid of the CMIP5term models for historical (1971–2000) and future (2070–2099) periods is determined based on the aridity indexterm·dom (AIterm·dom). AIterm·dom is calculated as the ratio between potential evapotranspirationterm·dom (PET; water demand) and precipitation (P; water supply): AIterm·dom = PET/P. The grids with AIterm·dom < 1 are classified as humid, 1 ≤ AIterm·dom < 2 as semi-humid, 2 ≤ AIterm·dom < 5 as semi-arid and AIterm·dom ≥ 5 as arid 56 , 57 . PET is computed by the Hargreaves–Samani methodterm·mth 58 which effectively incorporates solar radiation by its indirect estimation from minimum and maximum temperatures. The use of both minimum and maximum temperature avoids the PET overestimation in dry and hot climates by methods based on only mean temperature such as the Thornthwaite method 28 , 59 , 60 . To investigate the possible impact of climate change on climate regimesterm·key (aridity classifications), the change in the area of each class (% of total terrestrial land area) is calculated. Once the spatial distribution of the global climate regimesterm·key is acquired, the median change of extreme precipitationterm·dom and flood intensityterm·dom for each climate regimeterm·key is determined.

The significance of the changes is assessed per climate regimeterm·key using the signal-to-noise (S2Nterm·mth) ratio. In this way, the large internal variability of extremes at local scale 61 , 62 can be decreased, leading to more robustterm·mth regional results 63 . For extreme precipitationterm·dom intensity, S2Nterm·mth is computed by dividing the ensemble median of changes across the 24 GCMsterm·dom by the standard deviation of the multi-model changes. For flood intensityterm·dom, S2Nterm·mth is calculated first across all GCMsterm·dom for each individual IM per climate regimeterm·key and then the median across all IMsterm·dom is considered as S2Nterm·mth per climate regimeterm·key. A signal is significant at 90% and 95% confidence levels when S2Nterm·mth is larger 1.64 and 1.96, respectively. A similar procedure is applied for the robustness of the changes in extreme precipitationterm·dom and flood intensityterm·dom where change for each climate regimeterm·key is considered robustterm·mth when at least 75% of experiments agree on the sign of the change.

Assessment of uncertainty sources The uncertainty in the projected changes in the intensity of extreme precipitationterm·dom and flood events is also quantified for each model grid. The extreme precipitationterm·dom and flood ensembles include 48 (2 methods × 24 GCMsterm·dom) and 40 (2 methods × 4 GCMsterm·dom × 5 IMsterm·dom) experiments, respectively. The total uncertainty of flood changes expressed as the coefficient of variationterm·mth (CVterm·mth) of the changes across the full ensemble is decomposed into hazard quantification method, GCMterm·dom and IM uncertainties, while that of extreme precipitationterm·dom changes is split into hazard quantification method and GCMterm·dom uncertainties. The uncertainty for the components with a larger sample size tends to be larger than those with a smaller sample size 64 . To limit this bias, the variance decomposition-same sample size (VD-SSS 9 ) method is employed for the uncertainty quantification of the GCMterm·dom and IM components with larger sizes, while the conventional variance decomposition (VD) method is applied for the hazard quantification method uncertainty. The VD-SSS method uses an iterative sampling-theory based bootstrapping procedure where a sample of size n (equal to the smallest sample size among the uncertainty components) is first drawn randomly from the full population of size N (e.g., 24 for GCMsterm·dom). The CVterm·mth across the bootstrap samples is then estimated. This procedure is repeated a large number of times (1,000 iterations in this study) and the median of the empirical bootstrap distribution of sample CVterm·mth denotes the uncertainty. To understand how uncertainty differs between climate regions, both total and fractional uncertainties are computed per climate regimeterm·key.

Supplementary information Supplementary Information.