Using the standardized precipitation evapotranspiration index, this study examines the combined effects of El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) on global droughts in terms of magnitude, timing, and duration. The ENSO-affected drought hotspots are identified based on drought magnitude and probability of occurrence: five hotspots for El Niño (Amazon, India, central China, Indonesia, and eastern Australia) and four hotspots for La Niña (southeastern United States, southern South America, East Africa, and Southwest Asia). When ENSO and PDO are in phase, most of the hotspots exhibit an intensification and expansion of drought, more clearly at longer time scales (6-12 months), supporting previous studies. Interestingly, the in-phase PDO advances El Niño-induced drought onset by early summer of the previous year, whereas it delays the withdrawal of La Niña-induced drought until the end of the event year. This asymmetric response is found to be in part associated with the earlier start and later end of El Niño itself during warm PDO, which does not hold for the La Niña/cold PDO composites. Further analyses of the responses of precipitation (P) and potential evapotranspiration (PET) to different ENSO-PDO combinations suggest the important role of P reduction in determining drought magnitude and timing over most of the hotspots, with some contribution of enhanced PET to drier conditions over a few La Niña hotspots. It is also found that the PDO modulation of El Niño-induced drought occurs primarily through the eastern Pacific El Niño with a limited influence on the central Pacific El Niño.

The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME-mean) at monthly and seasonal time scales for the historical simulation over the period 1988-2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME-mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME-means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS-g3, IPSL-CM6A-LR, and IPSL-CM6A-LR-INCA, and one model in consecutive dry day (CDD) simulation, MPI-ESM-1-2-HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.