A record‐breaking extreme rainfall event, the highest amount recorded since 1866, hit Indonesia's capital, Jakarta, in early January 2020. This torrential rainfall was mainly caused by the convergence of moisture‐rich air due to an unusual blocking of cross‐equatorial northerly surge over Northwest Java by the southerly winds induced by a cyclonic flow over the Indian Ocean. This condition caused a local increase in the amount of water vapor over Jakarta that eventually led to the formation of clouds and heavy precipitation. In addition, the concurrent occurrences of convectively active phases of equatorial waves (Kelvin, TD‐type, and eastward propagating inertia‐gravity waves) and Madden‐Julian Oscillation during the event also partly contributed to the enhanced local moisture over the region by increasing low‐level moisture flux convergence. Together, these large‐scale dynamical drivers provided a convective environment that fostered the development of a massive rain‐producing mesoscale convective system and, consequently, extreme rainfall over the region.

Unusually long duration and heavy rainfall from 5 to 6 February 2021 caused widespread and devastating floods in Semarang, Central Java, Indonesia. The heavy rainfall was produced by two mesoscale convective systems (MCSs). The first MCS developed at 13Z on 5 February 2021 over the southern coast of Sumatra and propagated towards Semarang. The second MCS developed over the north coast of Semarang at 18Z on 5 February 2021 and later led to the first peak of precipitation at 21Z on 5 February 2021. These two MCSs eventually merged into a single MCS, producing the second peak of precipitation at 00Z on 6 February 2021. Analysis of the moisture transport indicates that the strong and persistent north-westerly wind near the surface induced by CENS prior to and during the event created an intensive meridional (southward) tropospheric moisture transport from the South China Sea towards Semarang. In addition, the westerly flow induced by low-frequency variability associated with La Nina and the tropical depression over the North of Australia produced an intensive zonal (eastward) tropospheric moisture transport from the Indian Ocean towards Semarang. The combined effects of the zonal and meridional moisture transport provided favorable conditions for the development of MCSs, and hence extreme rainfall over Semarang. These results provide useful precursors for extreme weather-driven hazard prediction in Semarang and the surrounding regions in the future.

Nusantara, Indonesia’s new capital city, experienced a rare extreme rainfall event on 27–28 August 2021. This heavy rainfall occurred in August, the driest month of the year based on the monthly climatology data, and caused severe flooding and landslides. To better understand the underlying mechanisms for such extreme precipitation events, we investigated the moisture sources and transport processes using the Lagrangian model HYSPLIT. Our findings revealed that moisture was mostly transported to Nusantara along three major routes: from Borneo Island (BRN, 53.73%), the Banda Sea and its surroundings (BSS, 32.03%), and Sulawesi Island (SUL, 9.05%). Overall, BRN and SUL were the main sources of terrestrial moisture, whereas the BSS was the main oceanic moisture source, having a lower contribution than its terrestrial counterpart. The terrestrial moisture transport from BRN was mainly driven by the large-scale high vortex flow, whereas the moisture transport from the SUL was driven by the circulation induced by boreal summer intraseasonal oscillation (BSISO) and low-frequency variability associated with La Niña. The near-surface oceanic moisture transport from BSS is primarily associated with prevailing winds due to the Australian monsoon system. These insights into moisture sources and pathways can potentially improve the accuracy of predictions of summer precipitation extremes in Indonesia’s new capital city, Nusantara, and benefit natural resource managers in the region.

The impact of the southerly surge’s interaction with the MJO on rainfall in this study was investigated using daily rainfall data from 2140 weather-observation stations. The southern surge, which coincided with the MJO, enhanced rainfall in the western research region, with Yogyakarta seeing the greatest increase at 4.69 mm/day. Meanwhile, the southern surge that occurred without the MJO increased rainfall in the eastern region, with West Nusa Tenggara seeing the greatest rise at 3.09 mm/day. However, the southerly surge has the effect of lowering rainfall in Jakarta, reaching −2.21 mm/day when the MJO is active and −1.58 mm/day when the MJO is inactive. The southerly surge causes extreme rainfall to only occur in a small part of certain areas, so it tends to significantly reduce the possibility of extreme rainfall. In the southern part of the Indonesian maritime continent, the southerly surge predominates over the MJO, supporting increased water vapor transport. Rainfall mostly increases in the afternoon and decreases in the morning when the southerly surge occurs, whether there is the MJO or not. Convective instability analysis indicates that SS increases precipitation, most likely by raising vertically integrated moisture flux convergence, with a correlation coefficient value of 0.82.

Spatial models are essential in the prediction of climate phenomena because they can model the complex relationships between different locations. In this study, we discuss an expanded spatial Durbin model with ordinary kriging on unobserved locations (ESDMOK) to predict rainfall patterns in Java Island. The classical spatial Durbin model needed to be expanded to obtain a parameter estimation for each location. We combined this with ordinary kriging because the data were not available in some locations. The data were taken from the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) website. Since climate data are big data, we implement a big data analytics approach, namely the data analytics life cycle method. As the exogenous variables, we used air temperature, humidity, solar irradiation, wind speed, and surface pressure. The authors developed an R-Shiny web applications to implement our proposed technique. Using our proposed technique, we obtained more accurate and reliable climate data prediction, indicated by the mean absolute percentage error (MAPE), which was equal to 1.956%. The greatest effect on rainfall was given by the surface pressure variable, and the smallest was wind speed.