A method to extend temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates.

• A Bayesian joint probability method is presented to calibrate reanalysis estimates. • The calibrated estimates are more accurate than the original reanalysis estimates. • The uncertainty spread produced from the method is shown to be reliable. Available high quality precipitation datasets generally cover only recent periods. To extend these datasets back in time is challenging due to historically lower rain gauge network density or poorer remote sensing. In this study, a Bayesian joint probability method is presented to extend the temporal coverage of high quality precipitation datasets by calibrating reanalysis estimates. Relationships between precipitation estimates from a high quality dataset and precipitation estimates from a reanalysis dataset are established by using data from the overlapping period of the two datasets. The relationships are then used to calibrate reanalysis estimates for the period when only reanalysis data is available. Rain gauge observations are also used to enhance the calibration. The method brings the reanalysis dataset to the same spatial resolution as the high quality dataset, corrects bias, and makes the reanalysis data more consistent with the high quality dataset. The calibrated estimates generated are presented in the form of ensembles to represent uncertainty. The method is applied to the Han River basin in China, using the CMPA high quality dataset and the ERA-interim reanalysis dataset. The method is shown to be highly effective in improving the quality of ERA-Interim. [ABSTRACT FROM AUTHOR]