Season-land-use heterogeneity Bayesian Three-Cornered Hat (SLH-BTCH) for precipitation fusion in ungauged and sparsely gauged regions

The source region of the Yangtze River, a core part of the “Asian Water Tower,” has sparse gauges, cold high elevation, and complex relief. Together with strong climatic variability, these factors cause large precipitation-estimation errors that undermine hydrological modeling and meteorological assessments. We introduce the Season–Land-Use Heterogeneity Bayesian Three-Cornered Hat (SLH-BTCH), an enhancement of BTCH. Data are grouped by season–land-use strata; within each group we estimate error covariance and then fuse products by weighted averaging, using only multi-source precipitation fields—no in-situ priors. Using CHIRPS, CMFD, TPHiPr, and CHM-PRE, we assess daily performance of the originals, BTCH, and SLH-BTCH against ground observations, and include an equal-weight average (EWA) baseline to gauge the benefit of grouped error modeling. Compared with the original products, BTCH and equal-weight averaging, SLH-BTCH yields event timing and seasonal precipitation more consistent with gauges while reducing storm-intensity bias and day-to-day noise across contrasting land-surface types. Around key headwater stations (Tuotuohe, Wudaoliang, Zhiduo) this sharpens damaging-storm signals and reduces false alarms, providing tighter basin water-balance closure and more reliable flood simulation, routing and dry-season water-availability estimates in ungauged, data-sparse sectors of the Tibetan Plateau. • SLH-BTCH fuses multi-source precipitation for data-scarce mountain headwaters. • Season–land-use grouping captures spatial–seasonal error heterogeneity in fusion. • SLH-BTCH outperforms BTCH and EWA, aiding flood and water-balance modelling.