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In contrast to the wet gets wetter and dry gets drier paradigm, here, using observations and climate model simulations, we show that the mean rainfall over the semi-arid northwest parts of India and Pakistan has increased by 10 to 50 percent during 1901 to 2015 and is expected to increase by 50 to 200 percent under moderate greenhouse gas (GHG) scenarios, e.g, SSP2 4.5. The GHG forcing primarily drives the westward expansion of the Indian summer monsoon (ISM) rainfall and is a result of a westward expansion of the inter-tropical convergence zone (ITCZ), facilitated by a westward expansion of the Indian Ocean warm pool. While an adaptation strategy to increased hydrological disasters is a must, harvesting the increased rainfall would lead to a significant increase in food productivity, bringing transformative changes in the socio-economic condition of people in the region.

Scientific basis for long-lead seasonal prediction of Indian summer monsoon rainfall (ISMR) critical for water resource and crop strategy planning is lacking. Using a new predictor discovery method, here we show that the depth of 20 degree isotherm (D20) is least influenced by atmospheric noise and that the 18-month lead forecasts of ISMR have high potential skill (r = 0.86). The high potential predictability is due to smaller initial errors associated with the 18-month lead initial conditions and their slow growth associated with the El Nino and Southern Oscillation (ENSO). The potential skill arises not only from the correlation between ISMR and large-scale slowly varying D20 but also contributed significantly by that with the interannual small-scale D20 anomalies indicating a seminal role of the nonlinearity on the potential predictability. It is, therefore, imperative that a nonlinear predictor discovery as well as nonlinear prediction model is essential for realizing this potential predictability., Comment: 44 pages, 6 figures, 11 extended figures, 1 table

In the backdrop of a revolution in weather prediction by Numerical Weather Prediction (NWP) models, quantitative prediction of intensity of heavy rainfall events and associated disasters has remained a challenge. Encouraged by compelling evidence of electrical influences on cloud/rain microphysical processes, here we propose a hypothesis that modification of raindrop size distribution (RDSD) towards larger drop sizes through enhanced collision-coalescence facilitated by cloud electric fields could be one of the factors responsible for intensity errors in weather/climate models. The robustness of the hypothesis is confirmed through a series of simulations of strongly electrified (SE) rain events and weakly electrified (WE) events with a convection-permitting weather prediction model incorporating the electrically modified RDSD parameters in the model physics. Our results indicate a possible roadmap for improving hazard prediction associated with extreme rainfall events in weather prediction models and climatological dry bias of precipitation simulation in many climate models.

The forecast of Indian monsoon droughts has been predicated on the notion of a season-long rainfall deficit linked to warm anomalies in the equatorial Pacific. Here, we show that in fact nearly half of all droughts over the past century were sub-seasonal, and characterized by an abrupt decline in late-season rainfall. Furthermore, the potential driver of this class of droughts is a coherent cold anomaly in the North Atlantic Ocean. The vorticity forcing associated with this oceanic marker extends through the depth of the troposphere, and results in a wavetrain which curves towards the equator and extends to East-Asia. This upper-level response triggers an anomalous low-level anticyclonic circulation late in the season over India. This teleconnection from the midlatitudes offers an avenue for improved predictability of monsoon droughts., Comment: 29 pages, 13 figures, 3 tables (Submitted July 2019)

Large socio-economic impact of the Indian Summer Monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather estimated low potential predictability limit (PPL) of seasonal prediction of the ISM, contributed significantly by 'internal' interannual variability was considered insurmountable. Here we show that the 'internal' variability contributed by the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g. El Nino and Southern Oscillation). This provides a scientific basis for predictability of the ISM rainfall beyond the conventional estimates of PPL. We establish a much higher actual limit of predictability (r~0.82) through an extensive re-forecast experiment (1920 years of simulation) by improving two major physics in a global coupled climate model, which raises a hope for a very reliable dynamical seasonal ISM forecasting in the near future.

The skill of the statistical as well as physics-based coupled climate models in predicting the El Ni\~no-Southern Oscillation (ENSO) is limited by their inability to represent the observed ENSO nonlinearity. A promising alternative, namely a deterministic nonlinear dynamical model derived from an observed ENSO timeseries, however, has remained elusive. Here we discover such a phenomenological nonlinear dynamical model that embodies known physical processes responsible for the self-sustained quasi-oscillatory character of the ENSO and its observed spectrum of variability. High predictive potential of the model is demonstrated and the intrinsic nonlinearity of the ENSO is shown to be critical for overcoming the Spring Predictability Barrier to a large extent. The unique methodology presented here has the potential for constructing similar models for other geophysical systems., Comment: Page 1-17 main text, Page 18 onwards Supplementary Information/Material