We quantify seasonal prediction skill of tropical winter rainfall in 14 climate forecast systems. High levels of seasonal prediction skill exist for year-to-year rainfall variability in all tropical ocean basins. The tropical East Pacific is the most skilful region, with very high correlation scores, and the tropical West Pacific is also highly skilful. Predictions of tropical Atlantic and Indian Ocean rainfall show lower but statistically significant scores.We compare prediction skill (measured against observed variability) with model predictability (using single forecasts as surrogate observations). Model predictability matches prediction skill in some regions but it is generally greater, especially over the Indian Ocean. We also find significant inter-basin connections in both observed and predicted rainfall. Teleconnections between basins due to El Niño-Southern Oscillation (ENSO) appear to be reproduced in multi-model predictions and are responsible for much of the prediction skill. They also explain the

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Peninsular Indian agriculture and drinking water availability are critically reliant on seasonal winter rainfall occurring from October to December, associated with the northeastern monsoon (NEM). Over 2016–2018, moderate-to-exceptionally low NEM rainfall gave rise to severe drought conditions over much of southern India and exacerbated water scarcity. The magnitude and dynamics of this drought remain unexplored. Here, we quantify the severity of this event and explore causal mechanisms of drought conditions over South India. Our findings indicate that the 3-year cumulative rainfall totals of NEM rainfall during this event faced a deficit of more than 40%—the driest 3-year period in ∼150 years according to the observational record. We demonstrate that drought conditions linked to the NEM across South India are associated with cool phases in the equatorial Indian and Pacific Oceans. Future changes in these teleconnections will add to the challenges of drought prediction.

Considering the wide use of the multi-model mean (MMM) on the seasonal time scale, this work examines its fidelity in simulating some important characteristics of the Indian summer monsoon using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. It is noted that the MMM captures the observed spatial pattern and annual cycle of surface air temperature to a great extent, but there are large biases in magnitude, particularly over north India. For precipitation, only the broad-scale features are captured and extreme large biases, of magnitude equal or higher than the seasonal mean precipitation, exist in the MMM. The simulation of trends in seasonal mean temperatures and precipitation is even less satisfactory than the climatological means. Several precipitation features, for example, low-to-moderate intensity precipitation events, orography-related rain bands, extreme events, are noted to improve with increasing resolution of the models, whereas, no such improvement is noted for temperatures. It is also noted that the improvement in CMIP5 MMM is marginal if compared with the best performing model from the group of models considered for analysis. There are several models that show similar skill as MMM, and therefore could be alternatively used for future projections. Moreover, using such individual models for Indian monsoon projections will also help us to understand the underlying mechanisms and processes by conducting targeted numerical experiments, which would otherwise be highly limited by approaches like MMM. Therefore, targeted efforts to improve some of these better models are required to gain more confidence in future projections of Indian monsoon.

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In the past, India has suffered severe socio-economic losses due to recurring floods and droughts during boreal summer (June–August). In this analysis, we estimate the chance of extreme summer rainfall, i.e. flood and drought over India for the present climate using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) method. This is the first application of the method to the hindcasts from multiple coupled atmosphere-ocean models. We first test individual models against the observed rainfall record over India and select models that are statistically indistinguishable from observations. We then calculate the chances of floods, droughts and unprecedented rainfall using 1669 realizations of summer precipitation from the selected set of models. It is found that the chance of drought is larger than the chance of flood in the present climate. There is a clear El Niño (La Niña) signal in dry (wet) summers and the occurrence of more frequent and intense droughts than floods in both models and observations is partly due to El Niño Southern Oscillation phase asymmetry. The chances of record-breaking drought and flood are 1.6% and 2.6%, respectively. There is also an estimated chance that a 30% rainfall deficit could occur around once in two centuries, which is far beyond the record deficit over India.