This study investigates how to use the gridded satellite datasets of observational precipitation to improve the performance of the climatological simulation by using the method of non-parametric quantile mapping (QM). The precipitation in Southeast Asia is simulated in 2001–2005 using the climate model of Weather Research and Forecasting (WRF). Two satellite datasets of observational precipitation, GSMaP and CHIRPS, are used for model training, simulation evaluation, and cross-validation. The evaluations of simulation and bias correction suggest that QM is able to perfectly correct the overall quantile distributions of the simulated precipitation, which is characterized by overestimation at most quantiles, especially for light and extreme precipitation. After the QM correction based on GSMaP (CHIRPS), the relative bias of the monthly average for all months is reduced from 39.3% to 4.1% (from 57.2% to 4.2%). The biases of spatial patterns are largely narrowed from 43.5% (59.4%) to 4.0% (2.5%) for annual-mean precipitation and from 43.5% (59.4%) to 4.0% (2.5%) for extreme precipitation. The results indicate that the QM correction based on the gridded satellite datasets outperforms the raw model output and greatly improves the estimates of the simulated precipitation.

High-resolution projections of near-surface winds are crucial for ocean circulation and sea level studies in Southeast Asia, a region characterized by complex coastlines and monsoon variability. This study evaluates the added value of dynamical downscaling using the Weather Research and Forecasting (WRF) model at 9 km resolution, driven by two CMIP6 global climate models (EC-Earth3 and MPI-ESM1-2-HR), to simulate 10 m wind climatology over the Southeast Asian seas. Comparisons were made against ERA5 reanalysis and the parent CMIP6 GCMs, focusing on seasonal mean patterns, interannual variability, and the annual cycle. The WRF simulations demonstrate substantial improvement in capturing the spatial structures of monsoonal winds and regional circulation features. Future wind projections under SSP2-4.5 and SSP5-8.5 scenarios reveal seasonally and spatially heterogeneous trends. The downscaled models project strengthening of winter monsoon winds over the Southeast Asian seas and a weakening of summer monsoon flows, with implications for upper ocean dynamics and regional sea level patterns. The leading modes of variability from EOF analysis indicate basin-wide wind anomalies modulated by periodic signals at ~1 year and ~2–7 years, likely driven by ENSO and the Asian monsoon. The power spectra of principal components reveal that internal variability persists across scenarios, though with increased signal-to-noise ratios (SNRs) in the WRF projections toward the end of the 21st century.

Accurate daily gridded precipitation estimates are needed to characterize spatial rainfall variability and support retrospective hydrological analysis in Southeast Asia, where complex rainfall regimes and sparse rain gauge coverage remain major challenges. This study develops an Extended Triple Collocation Analysis (ETCA)-based merged daily precipitation dataset for Southeast Asia using 15 multi-source gridded precipitation products for 2000–2014. The products include gauge-based, satellite-based, reanalysis-based, and merged datasets, all regridded to a common 10 km × 10 km grid. ETCA was applied to all 455 possible three-product combinations to estimate product-level reliability, expressed as the squared correlation coefficient and error variance at each grid cell. The results reveal substantial spatial variability in product reliability. The final ETCA-merged product was generated through pixel-wise reliability-based product selection, quantile-based distributional adjustment using the locally highest-ranked product as an internal reference, and equal-weight averaging of the selected adjusted products. Validation against GSOD daily observations shows that the ETCA-merged product achieves lower RMSD of 2.95 mm day⁻¹, compared with 2.96 mm day⁻¹ for the simple all-product ensemble and 3.01 mm day⁻¹ for the ensemble of the five most regionally reliable products. The corresponding temporal correlations are 0.79, 0.81, and 0.78, respectively. The merged product also improves the representation of high-percentile rainfall, although very intense rainfall remains underestimated. Spatial climatology and annual cycle analyses indicate that the ETCA-merged product preserves the main rainfall patterns and seasonal evolution of Southeast Asia while introducing local adjustments based on product reliability. These findings demonstrate that ETCA provides a useful framework for developing uncertainty-informed precipitation datasets in regions with sparse gauge observations and spatially heterogeneous product performance.