Prediction and Predictability of the Wet-Season Rainfall over Southeast India

TL;DR

This study explores the increasing variability and predictability of wet-season rainfall over Southeast India, highlighting the role of sea surface temperatures and long-term climate trends in improving forecast skill.

Contribution

It introduces a data-driven methodology that enhances seasonal rainfall prediction over Tamil Nadu by leveraging SST anomalies and climate interactions across regions.

Findings

Rainfall variability has increased due to higher surface temperatures and moisture convergence.

The rainy season lengthening is linked to earlier onset and delayed withdrawal of monsoon.

High potential for long-lead rainfall prediction up to 10 months using SST and climate interactions.

Abstract

The challenge in predicting sub-regional climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.