The characteristics of climatic rainfall variability in Indonesia are investigated using a double correlation method. The results are compared with empirical orthogonal function (EOF) and rotated EOF methods. In addition, local and remote responses to sea‐surface temperature (SST) are discussed. The results suggest three climatic regions in Indonesia with their distinct characteristics. Region A is located in southern Indonesia from south Sumatera to Timor island, southern Kalimantan, Sulawesi and part of Irian Jaya. Region B is located in northwest Indonesia from northern Sumatra to northwestern Kalimantan. Region C encompasses Maluku and northern Sulawesi. All three regions show both strong annual and, except Region A, semi‐annual variability. Region C shows the strongest El Niño–southern oscillation (ENSO) influence, followed by Region A. In Region B, the ENSO‐related signal is suppressed. Except for Region B, there are significant correlations between SST and the rainfall variabilities, indicating a strong possibility for seasonal climate predictions. March to May is the most difficult season to predict the rainfall variability. From June to November, there are significant responses of the rainfall pattern to ENSO in Regions A and C. A strong ENSO influence during this normally dry season (June to September) is hazardous in El Niño years, because the negative response means that higher SST in the NIÑO3 of the Pacific region will lower the rainfall amount over the Indonesian region. Analyses of Indonesian rainfall variability reveal some sensitivities to SST variabilities in adjacent parts of the Indian and Pacific Oceans. Copyright © 2003 Royal Meteorological Society

This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multi-model simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E–146.5° E, 14.8° S–27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976–2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December–January–February (DJF) and June–July–August (JJA) over future periods of the early (2011–2040), mid (2041–2070) and late twenty-first century (2071–2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10–20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10–30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

This paper documents the changes in climate extremes over Indonesia during the past three decades (1983–2012) based on a subset of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI). The extreme indices were calculated based on the quality controlled daily observational data (minimum and maximum temperature and precipitation) from 88 weather stations. Overall, we found significant and spatially coherent trends of warming in the temperature indices over Indonesia, consistent with other studies conducted at different countries within the Southeast Asia. The frequency of cool days (TX10p) and cools nights (TN10p) had decreased whereas warm days (TX90p) and warm nights (TN90p) were observed more frequently. Averaged over the country, the annual mean of daily maximum (TXmean) and minimum temperature (TNmean) had increased significantly by 0.18 and 0.30 °C decade⁻¹, respectively. Other temperature indices also showed significant warming trends. In contrast, trends in the precipitation extremes indices were generally not significant and less spatially coherent. However, a tendency towards wetter conditions was observed, in agreement with the results at the global scale. The daily precipitation intensity (SDII) had increased significantly over the country by 0.21 mm day⁻¹ decade⁻¹ during the period studied. At the regional scale, we observed a significant wetting trend in the annual highest daily amount (RX1day) and the rainfall amount contributed by the extremely very wet days (R99p) in the northern part of the country. The wetting trends of a number of extreme precipitation indices were depicted prominently in December–January–February (DJF) and/or March–April–May (MAM) seasons, both at country and regional levels. However, for the southern region of Indonesia, a drying tendency was observed for June–July–August (JJA), September–October–November (SON) and MAM.

The accuracy of three satellite rainfall products (TMPA 3B42RT, CMORPH and PERSIANN) was investigated through comparison with grid cell average ground station rainfall data in Indonesia, with a focus on their ability to detect patterns of low rainfall that may lead to drought conditions. Each of the three products underestimated rainfall in dry season months. The CMORPH and PERSIANN data differed most from ground station data and were also very different from the TMPA 3B42RT data. It proved possible to improve TMPA 3B42RT estimates by applying a single empirical bias correction equation that was uniform in space and time. For the six regions investigated, this reduced the root mean square error for estimates of dry season rainfall totals by a mean 9% (from 44 to 40 mm) and for annual totals by 14% (from 77 to 66 mm). The resulting errors represent 10% and 3% of mean dry season and annual rainfall, respectively. The accuracy of these bias corrected TMPA 3B42RT data is considered adequate for use in real-time drought monitoring in Indonesia. Compared to drought monitoring with only ground stations, this use of satellite-based rainfall estimates offers important advantages in terms of accuracy, spatial coverage, timeliness and cost efficiency.