From Climate Early Warning to Government Action: Anticipatory Governance during Indonesia's 2026 Extreme El Niño

Abstract

Extreme El Niño events are often treated as meteorological or agricultural problems, yet their consequences also test the anticipatory capacity of public administration. This study examines how climate early-warning information was translated into government action during Indonesia's 2026 extreme El Niño episode. Using qualitative secondary-data analysis, the study synthesizes official documents and updates issued by the Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG), the National Disaster Management Agency (BNPB), the Ministry of Agriculture, and other public institutions up to 2 September 2026, together with scholarly and OECD literature on anticipatory governance. The analysis uses six dimensions: foresight and signal detection, policy translation, inter-agency coordination, preventive resource mobilization, local implementation, and monitoring and adaptive learning. The findings show a strong forecasting and warning capacity. BMKG progressively escalated its alerts from a high probability of strong El Niño to confirmed very strong conditions, while government agencies-initiated weather modification, drought preparedness, water distribution, pumping, irrigation optimization, and cross-sector coordination. However, public evidence is substantially stronger for activities and inputs than for integrated outcome measurement. Significant drought and wildfire impacts continued to be reported, and publicly available documents provide limited evidence linking specific warnings, pre-agreed administrative triggers, interventions, and avoided losses. The article therefore identifies a warning-to-action accountability gap: Indonesia demonstrates substantial anticipatory activity, but the institutionalization and public measurement of the full warning-to-action chain remain incomplete. Strengthening trigger-based protocols, interoperable risk data, pre-arranged financing, local implementation indicators, and post-event learning would improve anticipatory governance for future climate extremes.