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.

Nusantara, the new capital city of Indonesia, and its surrounding areas experienced intense heavy rainfall on 15–16 March 2022, leading to devastating and widespread flooding. However, the factors triggering such intense heavy rainfall and the underlying physical mechanisms are still not fully understood. Using high-resolution GSMaP (Global Satellite Mapping of Precipitation) data, we show that a mesoscale convective system (MCS) was the primary cause of the heavy rainfall event. The rainfall peak occurred during the MCS’s mature stage at 1800 UTC 15 March 2022, and diminished as it entered the dissipation stage. To understand the large-scale environmental factors affecting the MCS event, we analyzed contributions from the MJO, equatorial waves, and low-frequency variability to column water vapor and moisture flux convergence. Results indicate a substantial influence of the MJO and equatorial waves on lower-level (boundary layer) meridional moisture flux convergence during the pre-MCS stage and initiation, with their contributions accounting for up to 80% during the growth phase. Moreover, while La Niña and the Asian monsoon had negligible impacts on MCS moisture supply, we find a large contribution from the residual term of the water vapour budget during the maturation and decay phases of the MCS. This suggests that local forcing (such as small-scale convection, local evaporation, land-surface feedback, and topography) also contributed to modulation of the intensity and duration of the MCS. The results of this study can help in our understanding of the potential causes of extreme rainfall in Nusantara and could be leveraged to improve rainstorm forecasting and risk management across the region in the future.

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%.

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.