As the main components of the atmospheric heat source/sink over the Tibetan Plateau (TP), up-to-date spatiotemporal fields of surface sensible heat flux and latent heat release by precipitation are vital for investigating the local land–atmosphere interaction and the effect of the thermal forcing of the TP on global weather and climate. This study recalculates the long-term daily dataset of surface sensible heat flux and latent heat release of condensation over the TP based on 293 routine meteorological observations, with the latest date being 31 December 2019. Most stations have adequate and valid records during the period 1981–2019, and the results for 1951–1980 are also calculated if the observations are available. Moreover, a brief evaluation of the climatology and long-term variation during 1981–2019 is conducted. By providing the most continuous and longest set of observational surface sensible heat flux and latent heat release of condensation data over the TP with a high degree of credibility, this new dataset will support research concerning the multi-timescale variation of diabatic heating/cooling over the TP and its remote influence. It is openly available on the LASG data-sharing platform (http://data.lasg.ac.cn/TPSHLH/).

The leading mode of the global sea surface temperature anomaly (SSTA) on the interannual time scale is a quasi‐quadrennial oscillation mode, corresponding to the El Niño–Southern Oscillation cycle. Using observational data diagnosis and numerical simulations, we show that the global SSTA during the quadrennial cycle has significant impacts on the anomaly of diabatic heating over the Tibetan Plateau (TP) in summer. Idealized atmospheric general circulation model (AGCM) experiments forced by the global SSTA with and without the thermal forcing of the TP diabatic heating anomaly reveal the relative contributions of the SSTA direct effect and the TP feedback to the anomalous response of the East Asian summer monsoon (EASM). During the quadrennial oscillation, in the developing year summer, the diabatic heating weakens over the whole TP, inducing suppressed EASM precipitation accompanied by an anomalous cyclone over the northwestern Pacific and an anticyclone in North China. In the decaying year summer, the TP heating, characterized by an anomalous negative‐positive pattern, induces an anomalous anticyclone in South China and an anomalous cyclone in North China. However, without the anomalous TP thermal forcing, in both developing and decaying years the EASM shows similar response patterns with an anomalous anticyclone over the northwestern Pacific, an anomalous cyclone in North China, and an enhanced main rainfall band. Therefore, the TP thermal feedback plays a key role in the asymmetric responses of the EASM to the global SSTA during the different stages, characterized by suppressed precipitation in developing years and an enhanced main rainfall band in decaying years.

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.

Landfalling typhoons frequently cause severe flooding and socioeconomic losses along the eastern coast of China, yet the relative influence of oceanic and storm-related factors on precipitation variability remains insufficiently understood. This study investigates the relationships among sea surface temperature (SST), translation speed, typhoon intensity, and precipitation characteristics for 23 typhoons that made landfall in Zhejiang Province during 2000–2025. Typhoon track, intensity, precipitation, and SST data were analyzed to examine pre-landfall evolution, precipitation variability across intensity categories, and the environmental factors associated with typhoon-induced precipitation. Results showed that SST generally decreased as typhoon approached the coast, with mean values declining from 27.9 °C offshore to 24.5 °C at landfall. Translation speed exhibited a non-linear evolution during the 120 h preceding landfall, with an average speed of 9.8 km h⁻¹ and a weak positive tendency toward the coast. Peak 24 h precipitation varied considerably among intensity categories. Severe typhoons (STY) produced the highest mean precipitation (120.9 ± 92.3 mm), whereas super typhoons (SuperTY) exhibited lower but more consistent rainfall totals. Correlation analysis revealed a weak relationship between translation speed and rainfall (r = −0.18), indicating that storm motion alone was insufficient to explain rainfall variability. SST showed a moderate negative correlation with rainfall (r = −0.53), highlighting the complex relationship between ocean thermal conditions and precipitation variability. These findings show that typhoon precipitation over Zhejiang is associated with the combined variability of storm intensity, ocean thermal conditions, atmospheric moisture availability, and coastal environmental conditions, providing scientific insights for understanding typhoon-related hazards under climate change.