Weather‐type‐conditioned calibration of Tropical Rainfall Measuring Mission precipitation over the South Pacific Convergence Zone

The South Pacific region is an area affected by characteristic precipitation patterns undergoing extreme events such as tropical cyclones and droughts. First, a daily weather typing of precipitation is presented, based on principal component analysis and k‐means clustering using precipitation and atmospheric circulation variables derived from sea‐level pressure and wind reanalysis fields. As a result, five weather types (WTs) are presented, able to capture distinct precipitation spatiotemporal patterns, interpretable in terms of salient regional climate features. Second, we undertake the calibration of the TRMM precipitation product using a set of rain gauge stations as reference and scaling and empirical quantile mapping (eQM) as calibration techniques. Furthermore, we build upon the weather‐type classification to compare the results with a WT‐conditioned calibration approach. Overall, our results underpin the need of adjusting the existing TRMM biases, mostly relevant for the upper tail of their distribution, and advocate the use of correction techniques able to deal with quantile‐dependent biases—such as eQM—instead of a simple scaling, in order to obtain a more realistic representation of extreme precipitation events. The conditioning has shown only a marginal added value over the simple approach, although this minor improvement may prove relevant for applications focused on extreme event analysis. Furthermore, the weather types created can be applied to a wide variety of conditioned analyses in this region. Five daily‐weather types (WTs) for precipitation are presented, interpretable in terms of salient regional climatic features of the South Pacific region. We undertake a calibration of the TRMM precipitation product on 7 representative rain gauge stations (period 1998‐2019) using scaling and quantile mapping techniques, and considering their conditioning to these WTs. The conditioned calibration has shown a marginal added value in the cross‐validation, although this improvement may prove relevant for applications focused on extreme event analysis.