In this paper, the ability of two joint bias correction algorithms to adjust biases in daily mean temperature and precipitation is compared against two univariate quantile mapping methods when constructing projections from years 1981–2010 to early (2011–2040) and late (2061–2090) 21st century periods. Using both climate model simulations and the corresponding hydrological model simulations as proxies for the future in a pseudo-reality framework, these methods are inter-compared in a cross-validation manner in order to assess to what extent the more sophisticated methods have added value, particularly from the hydrological modeling perspective. By design, bi-variate bias correction methods improve the inter-variable relationships in the baseline period. Cross-validation results show, however, that both in the early and late 21st century conditions the additional benefit of using bi-variate bias correction methods is not obvious, as univariate methods have a comparable performance. From the evaluated hydrological variables, the added value is most clearly seen in the simulated snow water equivalent. Although not having the best performance in adjusting the temperature and precipitation distributions, quantile mapping applied as a delta change method performs well from the hydrological modeling point of view, particularly in the early 21st century conditions. This suggests that retaining the observed correlation structures of temperature and precipitation might in some cases be sufficient for simulating future hydrological climate change impacts.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited