Multiscale assessment of Indian monsoon rainfall using ICON and CMIP6 model simulations

Abstract. Indian Summer Monsoon (ISM) rainfall is organized across multiple timescales, from diurnal convection to synoptic disturbances, intraseasonal oscillations, and the seasonal mean. Climate models often show different levels of skill at each of these timescales, raising an important question: how do scale-dependent biases shape overall monsoon variability? Here, we assess a medium-resolution (40 km), non-hydrostatic global model (ICON) together with five hydrostatic CMIP6-class models (CNRM, MPI, GFDL, MIROC6, and IITM-ESM; 50–190 km resolution). All simulations are evaluated in AMIP configuration against high-resolution IMERG observations during 1998–2014, allowing isolation of atmospheric sources of rainfall bias. Rainfall errors are strongly scale-dependent and exhibit clear land–ocean contrasts. At the diurnal scale, ICON reproduces amplitudes over the continent with a relatively small bias (∼ 5 %–10 %), whereas MPI overestimates land diurnal amplitude by more than 150 % with premature triggering. The CNRM and GFDL show early daytime convection and weak nocturnal rainfall, while MIROC6 and IITM-ESM exhibit reduced diurnal amplitude linked to convective and resolution limitations. Over the Bay of Bengal, ICON overestimates diurnal amplitude (∼ 60 %) and variance (∼ 180 %), whereas CMIP6 models underestimate nocturnal oceanic variability (amplitude < 40 %, variance < 60 %). At synoptic timescales (2–7 d), models differ in their ability to sustain organized monsoon disturbances. ICON and GFDL maintain realistic spatial structure with moderate suppression, while MPI underestimates synoptic variance by up to ∼ 70 %–80 %. Other models either has weakened synoptic activity or redistribute variability toward intermediate (10–20 d) bands. Across the ensemble, the 20–100 d intraseasonal band is systematically underestimated (by ∼ 30 %–60 %) in the AMIP framework, suggesting that coupled ocean–atmosphere feedbacks, among other factors, contribute to maintaining monsoon intraseasonal oscillations. Seasonal rainfall patterns reflect the combined effect of these multiscale biases. Models that maintain a balanced variance distribution across diurnal, synoptic, and intraseasonal bands show improved seasonal structure, whereas distortions at intermediate frequencies contribute to amplitude and migration errors. These results indicate that credible monsoon simulation depends not only on seasonal-mean accuracy but also on physically consistent variability across timescales. A scale-aware diagnostic framework is therefore essential for improving convective triggering, mesoscale organization, boundary-layer processes, and air–sea coupling in climate models.