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Climate model simulations typically exhibit a bias, which can be corrected using statistical approaches. In this study, a geostatistical approach for bias correction of daily precipitation at ungauged locations is presented. The method utilizes a double quantile mapping with dry day correction for future periods. The transfer function of the bias correction for the ungauged locations is established using distribution functions estimated by ordinary kriging with anisotropic variograms. The methodology was applied to the daily precipitation simulations of the entire CORDEX‐Africa ensemble for a study region located in the West African Sudanian Savanna. This ensemble consists of 23 regional climate models (RCM) that were run for three different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). The evaluation of the approach for a historical 50‐year period (1950–2005) showed that the method can reduce the inherent strong precipitation bias of RCM simulations, thereby reproducing the main climatological features of the observed data. Moreover, the bias correction technique preserves the climate change signal of the uncorrected RCM simulations. However, the ensemble spread is increased due to an overestimation of the rainfall probability of uncorrected RCM simulations. The application of the bias correction method to the future period (2006–2100) revealed that annual precipitation increases for most models in the near (2020–2049) and far future (2070–2099) with a mean increase of up to 165mm⋅a−1 (18%). An analysis of the monthly and daily time series showed a slightly delayed onset and intensification of the rainy season. [ABSTRACT FROM AUTHOR]

Abstract: Sustainable decisions in water resources management require scientifically sound information on water availability. Central support in hydrological decision making arises from hydrological modelling which in turn depends on meteorological input. This work intends to show how integration of atmospheric science and hydrology, particularly the joint modelling of atmospheric and terrestrial water cycle, allows to provide decision support for two fundamental problems in sustainable water management: (1) the impact of global climate change on water availability, and (2) the near-real time estimation of recent resources and fluxes. The performance of joint atmospheric-hydrological simulations and its application for decision support is demonstrated for the Volta Basin of West Africa. First, the impact of global warming on water availability in the Volta Basin is assessed by joint regional climate-hydrology simulations. Time slices of ECHAM4 global climate scenario IS92a are dynamically downscaled to a resolution of 9×9km2 with the mesoscale meteorological model MM5 and the terrestrial water balance is subsequently calculated by the distributed hydrological model WaSiM. Results indicate a slight increase in total annual precipitation by 5%, but also a significant decrease of precipitation in April (in total 20%, for specific regions up to 70%), at the beginning of the rainy season. Terrestrial water balance variables react highly nonlinear to changes in precipitation and temperature. Second, the performance of joint atmospheric-hydrological simulations as a tool for near-real-time, model based water monitoring system for the White Volta subcatchment is assessed. Operational available GFS (Global Forecasting System) global analyses are dynamically downscaled to 9×9km2 resolution and meteorological fields subsequently applied for calculating the terrestrial water balance. Albeit a bias in precipitation simulations can be observed when comparing to point measurements, modelled river discharge agrees well with observations in 2004. It is concluded that particularly in areas with weak infrastructures, where few meteorological observations are available (or only in long delay), atmospheric models can provide reasonable meteorological information for hydrological modelling and therefore facilitate decision support. [Copyright &y& Elsevier]