A variance-upscaling quantile mapping method for gridded precipitation bias correction

Abstract

Accurate estimation of precipitation in mountainous regions remains a challenge. Quantile Mapping (QM) is a method used extensively to correct gridded precipitation from numerical models or satellite remote sensing. The method can match the intensity histogram of a given gridded precipitation to that of the observation. However, the widely existing oversmoothed effect, a precipitation time series variability representativeness error, in observational gridded datasets may be inherited in the corrected results, impacting the assessment of extreme precipitation events. To address this issue, this study introduces a variance-upscaling QM method (VUQM). Unlike previous methods that obtain frequency parameters by interpolating rain gauge data, the new method creates a model to transform the precipitation time series variance at the rain gauge point level to the model's grid level. When compared to correct ERA5-Land reanalysis precipitation, the results show that the variance using the VUQM is 1.6 times larger than that obtained using QM alone, and it is 2 times larger in the 95th percentile. This indicates that the new method significantly reduces the oversmoothed effect and preserves precipitation variation. Random simulations show that low station density only introduces uncertainty, not theoretical bias in VUQM. In contrast, the variance biases in gridded datasets constructed from areal average or interpolation cannot be avoided, particularly in regions with significant spatial heterogeneity. This suggests that VUQM is well-suited for use in the Qinghai-Tibetan Plateau (QTP) compared with the old methods. Overall, this method sheds some light on mountainous precipitation bias correction.