Evaluating the Fidelity of GraphCast AI Forecasts for the Indian Summer Monsoon: A Climatological Assessment Against ERA-5 Reanalysis and IMERG Observations

arXiv:2607.11905v1 Announce Type: new Abstract: The Indian Summer Monsoon (ISM) represents one of the most consequential and dynamically complex phenomena in the global climate system, yet its prediction remains challenging for both physics-based and data-driven models. This study evaluates Google's GraphCast, a machine-learning-based global weather prediction system, against ERA5 reanalysis and IMERG precipitation observations for the boreal summer season (June-September, JJAS) over 2021-2024. We analyze deterministic GraphCast forecasts initialized at 00 UTC using four 6-hourly lead times, composited for +24 h, +48 h, and +72 h lead performance across the ISM domain. The evaluation includes climatological mean state, rainfall intensity distribution, thermodynamic drivers (DTT, Q1, Q2), monsoon dynamics, and variability across multiple timescales. Results show GraphCast reproduces the broad spatial pattern of monsoon rainfall and the annual cycle with good fidelity at short leads, but exhibits a domain-averaged wet bias over the core monsoon region and strong suppression of rainfall variance across nearly all timescales (regional power-spectrum variance ratio ~0.14 relative to IMERG). The rainfall intensity distribution is right-shifted and compressed, with moderate-heavy rainfall (95th percentile) biased wet and extreme events underrepresented. These biases are accompanied by a deficient lower-tropospheric Q1 profile and degraded northward-propagating intraseasonal variability. Overall, GraphCast shows a deterministic smoothing signature in precipitation, providing key diagnostics for next-generation AI weather models and their use in tropical extended-range forecasting.