To improve the quantitative estimation of solid precipitation in snowy mountainous regions of Japan, we evaluated and corrected the systematic underestimation in the APHRO_JP gridded daily precipitation dataset. Two major sources of bias were addressed: wind-induced gauge undercatch and the poor representation of climatological precipitation due to the scarcity of high-elevation observations. To mitigate the latter, we refined the climatology using output from a high-resolution non-hydrostatic regional climate model (NHRCM) developed by the Japan Meteorological Agency and applied point-preserving ratio interpolation, in which the ratio of daily precipitation to the corresponding daily climatology is interpolated while preserving observed station values.

  We conducted four precipitation analyses, combining the presence or absence of wind-effect correction with model-based (NHRCM) climatology. These were evaluated using two independent validation methods: (1) snow weight observations at seven SW-Net sites (elevation range: 255–1310 m) and (2) water balance analysis in four dam catchments (mean elevation range: 727–1073 m). On average, wind-effect correction and climatology refinement reduced RMSE by 9.6% and 8.4%, respectively. In the water balance analysis, climatology refinement led to an average precipitation increase of approximately 18%, while wind-effect correction contributed an additional 7%.

  Both validation methods demonstrated that climatology refinement had a greater impact than wind correction, particularly in high-elevation regions. These results emphasize that model-based climatology, combined with point-preserving ratio interpolation, can substantially improve precipitation estimates in data-sparse, snow-covered mountainous areas. The corrected gridded datasets developed in this study will be publicly available through the APHRODITE project website.

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 We investigated the role of air-sea interaction in the intra-seasonal fluctuation of the monsoon trough (MT) over the Western North Pacific (WNP) during boreal summer by comparing a global non-hydrostatic atmospheric numerical model “NICAM” and its ocean coupled version “NICOCO.” We conducted a series of 10-member ensemble experiments from 10 July to 1 September 2020, with NICOCO, NICAM forced by sea surface temperature (SST) that only fluctuates along the climatological seasonal cycle (NICAM-A), and NICAM forced by daily-mean SST simulated in the NICOCO experiments (NICAM-N). All models simulated small eastward extension of the MT, characteristic under a La Niña condition. NICAM-N slightly overestimated MT activities. Compared to NICOCO, NICAM-A significantly underestimated the northward propagation speed of the convective envelope associated with the MT. This difference was attributed to variations in SST fluctuations. Clouds associated with the MT reflect downward shortwave radiation, reducing ocean heating. Additionally, when convective activities intensify over the WNP, cyclonic circulations associated with convections accelerate (decelerate) the background westerly/southwesterly on the southern (northern) side of convections, enhancing (reducing) latent heat release. Consequently, NICOCO simulated dominant negative net downward surface heat flux and resultant negative SST anomaly on the southern side, whereas NICAM-A did not simulate such fluctuations since the effect of the atmosphere to the ocean is not considered. The meridionally asymmetric SST anomaly structure simulated in NICOCO likely supports the northward propagation of convections. These results suggest that air-sea interaction is important in predicting the fluctuation of the MT and convections over the WNP.

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 Tropical waves shape weather across equatorial regions, yet their representation in global models remains a formidable challenge. This study investigates how model fidelity depends on two key factors: horizontal resolution (120 km to 3.75 km) and the treatment of convection (parameterized vs. explicit). The 3.75-km simulation with explicit convection most faithfully reproduces wave structures and wave-driven rainfall. Interestingly, a 15-km simulation with an alternative parameterization scheme achieves comparable structural fidelity and wave-driven rainfall, but at the cost of a pronounced overall precipitation bias. Moreover, simulations that excel at capturing wave structure tend to perform poorly in reproducing propagation speed, whereas parameterized simulations—though less realistic in structure and rainfall—represent speed more accurately. This trade-off highlights both the promise and the limitations of global kilometer-scale models and underscores the need for continued model development to advance tropical weather and climate prediction.

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