Water table depth (WTD) is the predominant biophysical control over the occurrence of peat and forest fires in tropical peatlands. In Indonesia, prolonged droughts caused by El-Niño and/or positive Indian Ocean Dipole (IOD), exacerbated by extensive peat drainage for agriculture and plantation establishment, can promote severe peatland fires by lowering WTD and hence desiccating surface and sub-surface peats. The severe drought episode of late 2015 across Indonesia, caused by a strong El Nino and a positive IOD, led to a major and damaging increase in peatland fires, highlighting an urgent need to develop operational systems to forecast potentially severe fire events to mitigate the impacts of fire and haze. The 2002 ASEAN Agreement on Transboundary Haze Pollution, signed and ratified by a total of 10 ASEAN states, including Indonesia, identifies a critical need for such systems based on near-time climate projections. However, such systems have not yet been developed. While an operational early warning system for forecasting dangerous burning conditions in Indonesia is currently within reach using state-of-the-art modelling tools, such as the ECMWF’s System 5 seasonal forecast model (SEAS5), development is still hampered by insufficient knowledge about the influence of fluctuations in peat moisture on fire, particularly during periods of extreme drought. The main objectives of this study were: i) to deploy a process-based ecosystem model “ecosys” to study how WTD and peat moisture profiles change in tropical peatlands across Riau province, Sumatra, in response to drought and land cover change, focusing on the 2015 drought; and ii) to examine whether those changes could have been predicted using SEAS5. Model spin-up from 2008-2014 was driven by inputs from ECMWF’s climate reanalysis data (ERA5), followed by 3 parallel simulations for 2015, driven by ERA5, ERA5 climatology, and SEAS5 hindcasts. Model outputs of peat moisture profiles and WTD showed how peat moisture and WTD were significantly affected by weather and land uses during the dry season of 2015 which were corroborated well against data from Soil Moisture Active Passive satellite and site-level monitoring networks. Our work is a pioneering attempt to perform large-scale process-based modelling to predict seasonal variations in tropical peatland WTD.