This paper describes the structure and evolution of northward propagating mesoscale convective systems (MCSs) observed over the tropical western Pacific on 15 June 2005. A wedge‐shaped convective cloud area, consisting of three groups of MCSs, was generated near the equator to the north of New Guinea at around 0000 LST on 15 June, and one of the groups of MCSs propagated toward Palau (7°–8°N). Dropsonde analysis revealed that the MCSs developed at the northern edge of a cold air mass that had a horizontal scale of more than 400 km. Global objective analysis data from the Japan Meteorological Agency showed that the cold air mass observed by the dropsonde analysis originated in a land breeze from New Guinea. The cold air mass was initially formed by cold advection from the south and was maintained by cooling with the MCSs. When the initial MCSs were generated along the land‐breeze front, an easterly wave, which was accompanied by low‐level southerly wind and a low‐pressure area, was located near Palau. The cold air mass associated with the land breeze between 132° and 137°E was locally extended toward the center of the low‐pressure area. The results suggest that the easterly wave located around Palau helped locally promote northward cold advection from New Guinea, inducing the long‐distance northward propagation of MCSs generated along the land‐breeze front.

Flood early warning systems (FEWS) are crucial for flood risk management; however, several catchments in the developing world are still far behind in all aspects of FEWS and thus, they encounter devastating damage recurrently due to limitations in data, knowledge, and technologies. This paper presents a catchment-scale integrated flood information system by incorporating present-day multi-platform data and technologies (e.g., ground and satellite rainfall observation, ensemble rainfall forecasts, and flood simulation) and evaluates their performance in a poorly gauged prototype basin (i.e., the Kalu River basin). Satellite rainfall products obtained in real time (GSMaP-NOW) and near-real time (GSMaP-NRT) can detect heavy rainfall events well and bias-corrected products can further improve rainfall estimations and flood simulations. Particularly, GSMaP-NRT, which outperformed GSMaP-NOW in both rainfall and discharge estimations, is suitable for near-real-time flood-related applications. Ensemble rainfall forecasts showed good performance in predicting alarming signals of heavy rainfall and peak flow with uncertainties in the amounts and timings of the events. Information derived from both satellite and ensemble forecasts on heavy rainfall, simulated flood signals, and their possible range of probabilities is promising and can help minimize the data gaps and improve the knowledge and technology of experts and policy-makers in poorly gauged basins.

Enhancing flood resilience, including the development of social capacity and early warning systems, in addition to structural measures, is one of the key solutions to mitigating flood damage, which will be more intensified in the future due to climate change. This study was conducted to develop a comprehensive methodology for enhancing flood resilience by improving society-wide disaster literacy under the governance formed through the active participation of all levels of stakeholders in Davao City, Philippines. Specifically, the development of the Online Synthesis System for Sustainability and Resilience, which integrates different disciplines, and the fostering of Facilitators, whose role is to interlink the science community and society, were implemented in a co-designing manner by the collective governance body. The development of basin- and barangay-scale hydrological models realized real-time flood forecasting and climate change impact assessment to identify intensified flood risk under the future climate. Co-designed e-learning workshops were held to foster about thirty Facilitators and help them produce twenty-one risk communication plans and workshop designs for fourteen barangays considering geographic, demographic, economic, and social features that they can utilize for public dissemination related to climate change adaptation to the target audiences in society. This paper presents a practical method to enhance flood resilience, demonstrating that the synthesis of science-based knowledge and human resource development can fill the gaps between the science community and society.

Heavy rainfall due to tropical cyclones (TCs) in the North Indian Ocean (NIO) adversely impacts nations frequently. Though extensive research has focused on TCs in the NIO, less attention has been given to the connection between TCs and extreme events in Sri Lanka. This study examined atmospheric characteristics during sixteen extreme events, focusing on linkages between TCs, the Indian Ocean Dipole (IOD), and mechanisms behind heavy rainfall associated with TCs over Sri Lanka. The results showed that in the pre-monsoon period, TCs move northward with high water vapor (WV) content accumulated in the Southern Hemisphere. This main WV flow over the equatorial Indian Ocean (EIO) is connected with TCs, causing considerable damage in the southwestern part of Sri Lanka. During negative IOD years, strong westerly winds create cyclonic circulations on either side of the equator. Conversely, during the post-monsoon period, the IOD phase has no significant effect. TCs generally followed westward tracks, supported by winds from the Northern Hemisphere, and caused heavy rainfall in the Eastern, Northern, and Northcentral provinces in Sri Lanka. These TCs are isolated from the main WV flow over EIO. Such observed common characteristics during pre-monsoon and post-monsoon seasons are key factors contributing to extreme rainfall in Sri Lanka.

The Philippines is one of the most climate-vulnerable countries in the world, with Central Luzon often experiencing extreme rainfall, floods, and droughts that are expected to worsen with ongoing warming. Using regional evidence from CORDEX–SEA (CMIP5) and CMIP6 multi-model ensembles, which show stronger rainfall extremes across Southeast Asia, this study produces basin-scale projections for the Pampanga River Basin and the Pasig-Marikina-Laguna-Lake Basin. High-resolution (5 km) climate projections were created with the Weather Research and Forecasting (WRF) model, dynamically downscaled from MRI-AGCM 3.2H (60 km) and 3.2S (20 km), and bias-corrected using the quantile-mapping method. The study analyzed projected changes in seasonal rainfall, heavy-rainfall indices (≥50 mm/day; 95th–99th percentiles), drought indicators (CDD, CWD), and design rainfall (from annual maxima) under RCP2.6 and RCP8.5 scenarios. The results show a clear intensification of heavy rainfall in both basins, especially during the southwest monsoon and under RCP8.5, with annual-maximum rainfall distributions indicating a consistent rise in extreme values and higher design rainfall for all return periods. Spatial patterns vary by basin: Pampanga has the largest increases in the lower western catchment, while the Pasig-Marikina-Laguna-Lake Basin shows a more uniform increase across the basin. At the same time, more consecutive dry days (CDD) and fewer consecutive wet days (CWD) suggest increased hydroclimatic variability, with wetter wet seasons and drier dry periods. These basin-specific, bias-corrected projections offer physically consistent data for flood and drought hazard assessment, agricultural and economic modeling, and climate-resilient water infrastructure design.