The Conditional Bias of Extreme Precipitation in Multi-Source Merged Data Sets

Multi-source data merging via weighted average (WA) is widely applied to enhance large-scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events-undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi-source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data-merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA-based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks.