Taiwan's most significant natural hazards are caused by hydrological extremes resulting from excessive precipitation. The threat of extreme precipitation is posed by several different types of weather patterns that affect Taiwan. This study examined the bi‐decadal changes in rainfall by defining an extreme precipitation occurrence (EPO) for a range of event durations from 1 to 24 hr. Three major weather types affecting EPO in Taiwan were identified from 1993 to 2015: the front type consisting of either a frontal zone or convective systems developing with an apparent Meiyu cloudband, diurnal rainfall events when no apparent synoptic features are present, and a tropical cyclone (TC) type according to the maximum sustained wind radius of a TC. Results show that TC‐type events have the greatest overall contribution to EPO at longer (>6 hr) durations. Diurnal/afternoon convection events contribute most to the shorter (6 hr) EPO associated with the TC‐type events in summer. The distinction between EPO trends for the entire island of Taiwan and for the Taipei metropolitan area alone (northern Taiwan, population of 7 million) were compared, and an intriguing interannual variation is reported in the TC‐type EPO associated with the TC season 1 year to a year and half just before an El Niño–Southern Oscillation event. The analysis here provides refined statistical distributions of extreme rainfall, and these can contribute to the revision of governmental definitions for weather disasters that are used in mitigation and response strategies.

This study analyzed the temporal/spatial distribution of the high‐temperature extremes (HTEs) in Taiwan. Three major weather types for HTEs were identified: the southerly/southwesterly (S/SW) (47.6%), western North Pacific subtropical high (SH) (35.9%), and tropical cyclone (16.5%). The S/SW type features anomalous warm advection in lower atmosphere and anomalous subsidence in all the altitudes around Taiwan. Different from the S/SW type, the SH type warms the surface primarily with increasing downward shortwave radiation. In June, the S/SW type was the most common. In July, both the S/SW and SH types dominated. In August, the tropical cyclone and SH types were more common. The subseasonal variation of extreme hot days (EHDs) for these three weather types is examined. Taipei and Dawu were two HTE hot spots in Taiwan. EHDs in Taipei occurred mostly in July and August, with the SH being the major type. Most EHDs in Dawu occurred in June, with the S/SW being the predominant type. These differences likely resulted from not only the subseasonal monsoon variation but also geographical differences. The interannual variability of the summer EHDs in Taiwan was found to be related to the phase and intensity of the El Niño‐southern oscillation events. The number of EHDs increased in the El Niño decaying summers and decreased in the La Niña decaying summers. The correlation between the number of summer EHDs and the Niño 3.4 SST anomaly reaches its peak in the previous winter (November/December). The associated mechanisms were discussed in the article.

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.