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This study aims to shed light on the general abilities of qm methods in correcting the bias of both temperature and rainfall variables, both of which can have direct and indirect. The method can match the. Quantile mapping (qm) is a method used extensively to correct gridded precipitation from numerical models or satellite remote sensing.
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By matching the empirical cumulative distribution functions (cdfs) of the two datasets, eqm corrects distributional biases across the entire range of precipitation values. In this study, we used storm catalogs, sst, and a stochastic bias correction approach to assess the upper tail of precipitation simulated by a convectionpermitting rcm. This method uses a parametric probability distribution to describe observations.
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