Articles | Volume 24, issue 6
Hydrol. Earth Syst. Sci., 24, 2963–2979, 2020
Hydrol. Earth Syst. Sci., 24, 2963–2979, 2020

Research article 08 Jun 2020

Research article | 08 Jun 2020

Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections

Nicholas J. Potter et al.

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Cited articles

Addor, N. and Seibert, J.: Bias correction for hydrological impact studies – beyond the daily perspective, Hydrol. Process., 28, 4823–4828,, 2014. 
Andrys, J., Lyons, T. J., and Kal, J.: Evaluation of a WRF ensemble using GCM boundary conditions to quantify mean and extreme climate for the southwest of Western Australia (1970–1999), Intl. J. Climatol., 36, 4406–4424,, 2016. 
Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM precipitation to produce unbiased climate change scenarios over large areas and small, Water Resour. Res., 48, W09502,, 2012. 
Bell, V. A. and Moore, R. J.: The sensitivity of catchment runoff models to rainfall data at different spatial scales, Hydrol. Earth Syst. Sci., 4, 653–667,, 2000. 
Bennett, J. C., Grose, M. R., Corney, S. P., White, C. J., Holz, G. K., Katzfey, J. J., Post, D. A., and Bindoff, N. L.: Performance of an empirical bias-correction of a high-resolution climate dataset, Int. J. Climatol., 34, 2189–2204,, 2014. 
Short summary
There is a growing need for information about possible changes to water resource availability in the future due to climate change. Large-scale outputs from global climate models need to be translated to finer-resolution spatial scales before hydrological modelling. Biases in this downscaled data often need to be corrected. We show that usual bias correction methods can retain residual biases in multi-day occurrences of rainfall, which can result in biases in modelled runoff.