EGU2020-10699, updated on 13 Jan 2022
EGU General Assembly 2020
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments

Camila Alvarez-Garreton1,2, Hylke Beck3, Eric Wood3, Tim R. McVicar4,5, Mauricio Zambrano-Bigiarini2,6, Oscar M. Baez-Villanueva7,8, Justin Sheffield9, and Dirk N. Karger10
Camila Alvarez-Garreton et al.
  • 1Universidad Austral de Chile, Valdivia, Chile
  • 2Center for Climate and Resilience Research (CR2), Santiago, Chile (FONDAP 15110009)
  • 3Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, USA
  • 4CSIRO Land and Water, Black Mountain, Canberra, ACT, Australia
  • 5Australian Research Council Centre of Excellence for Climate Extremes, Canberra, ACT, Australia
  • 6Department of Civil Engineering, Universidad de La Frontera, Temuco, Chile
  • 7Institute for Technology and Resources Management in the Tropics and Subtropics (ITT), Technology Arts Sciences TH Koln, Cologne, Germany
  • 8Faculty of Spatial Planning, TU Dortmund University, Dortmund, Germany
  • 9School of Geography and Environmental Science, University of Southampton, Southampton, United Kingdom
  • 10Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland

We introduce a set of global high-resolution (0.05) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the “true” long-term P using a Budyko curve, an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies (WorldClim V2, CHELSA V1.2, and CHPclim V1), after which we used random forest regression to produce global gap-free bias correction maps for the climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors based on gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. Additionally, all climatologies underestimate P at latitudes > 60N, likely due to gauge under-catch. Exceptionally high long-term correction factors (> 1.5) were obtained for all three climatologies in Alaska, High Mountain Asia, and Chile — regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected World-Clim V2 is 862 mm yr−1 (a 9.4 % increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias CORrection (PBCOR) dataset — downloadable via

How to cite: Alvarez-Garreton, C., Beck, H., Wood, E., McVicar, T. R., Zambrano-Bigiarini, M., Baez-Villanueva, O. M., Sheffield, J., and Karger, D. N.: Bias correction of global high-resolution precipitation climatologies using streamflow observations from 9372 catchments , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10699,, 2020.