Indian Monsoon Rainfall Amount Forecast: Network-based Approach and Climate Change Benefits

The Indian summer monsoon rainfall (ISMR) has decisive influence on India's agricultural output and economy. Extreme departures from the normal seasonal amount of rainfall can cause severe droughts or floods, affecting Indian food production and security.  Despite the development of sophisticated statistical and dynamical climate models, a long-term and reliable prediction of ISMR has remained a challenging problem.  Towards achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near surface air temperature field. We uncover that some characteristics of the directed and weighted climate networks can serve as good early warning signals for ISMR forecasting. The developed prediction method can produce a forecast skill of 0.5, by using the previous calendar year's data (5-month lead-time). The skill of our ISMR forecast is comparable to the best statistical and dynamical forecast models, which start in May or June. We reveal that global warming  affects climate network, by enchanting cross-equatorial teleconnections between Southwest Atlantic and North Asia-Pacific, which significantly impacts on  global precipitation. Remarkably, the consequences of climate change lead to improving the prediction skills. We discuss the underlying mechanism of our predictor and associate it with network--delayed--ENSO and ENSO--monsoon connections. Moreover, we find out that this approach is not limited to prediction of all India rainfall but also can be applied to forecast the Indian homogeneous regions rainfall. Our network-based approach developed in the present work provides a new perspective on the regional forecasting of the ISMR, and can potentially be used as a prototype for other monsoon systems.