Comparing skill of historical rainfall data based monsoon rainfall prediction in India with NWP forecasts

The Indian summer monsoon is a highly complex and critical weather system that directly affects the livelihoods of over a billion people across the Indian subcontinent. Accurate short-term forecasting remains a major scientific challenge due to the monsoon's intrinsic nonlinearity and its sensitivity to multi-scale drivers, including local land-atmosphere interactions and large-scale ocean-atmosphere phenomena. In this study, we address the problem of forecasting daily rainfall across India during the summer months, focusing on both one-day and three-day lead times. We use Autoformers - deep learning transformer-based architectures designed for time series forecasting. These are trained on historical gridded precipitation data from the Indian Meteorological Department (1901--2023) at spatial resolutions of 0.25^circ × 0.25^circ, as well as 1^circ × 1^circ. The models also incorporate auxiliary meteorological variables from ECMWFs reanalysis datasets, namely, cloud cover, humidity, temperature, soil moisture, vorticity, and wind speed. Forecasts at 0.25^circ × 0.25^circ are benchmarked against ECMWFs High-Resolution Ensemble System (HRES), widely regarded as the most accurate numerical weather predictor, and at 1^circ × 1^circ with those from National Centre for Environmental Prediction (NCEP). We conduct both nationwide evaluations and localized analyses for major Indian cities. Our results indicate that transformer-based deep learning models consistently outperform both HRES and NCEP, as well as other climatological baselines. Specifically, compared to our model, forecasts from HRES and NCEP model have about 22% and 43% higher error, respectively, for a single day prediction, and over 27% and 66% higher error respectively, for a three day prediction.