Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow Observations from 9372 Catchments

Abstract

AbstractWe 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-termPusing a Budyko curve, which is an empirical equation relating long-termP,Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-artPclimatologies [the “WorldClim version 2” database (WorldClim V2); Climatologies at High Resolution for the Earth’s Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for thePclimatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimatePover parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimatePat latitudes >60°N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all threePclimatologies 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 usedPdatasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimatePover Chile, the Himalayas, and along the Pacific coast of North America. MeanPfor the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr−1(a 9.4% increase over the original WorldClim V2). The annual and monthly bias-correctedPclimatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online (http://www.gloh2o.org/pbcor/).