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A global forecast system model at a horizontal resolution of T1534 (∼12.5 km) has been evaluated for the monsoon seasons of 2016 and 2017 over the Indian region. It is for the first time that such a high-resolution global model is being run operationally for monsoon weather forecast. A detailed validation of the model therefore is essential. The validation of mean monsoon rainfall for the season and individual months indicates a tendency for wet bias over the land region in all the forecast lead time. The probability distribution of forecast rainfall shows an overestimation (underestimation) of rainfall for the lighter (heavy) categories. However, the probability distribution functions of moderate rainfall categories are found to be reasonable. The model shows fidelity in capturing the extremely heavy rainfall categories with shorter lead times. The model reasonably predicts the large-scale parameters associated with the Indian summer monsoon, particularly, the vertical profile of the moisture. The diurnal rainfall variability forecasts in all lead times show certain biases over different land and oceanic regions and, particularly, over the northwest Indian region. Although the model has a reasonable fidelity in capturing the spatiotemporal variability of the monsoon rain, further development is needed to enhance the skill of forecast of a higher rain rate with a longer lead time.

General Circulation Models (GCMs) have for decades exhibited difficulties in modelling the diurnal cycle of precipitation (DCP). This issue can be related to inappropriate representation of the processes controlling sub-diurnal phenomena like convection. In this study, 11 single-column versions of GCMs are used to investigate the interactions between convection and environmental conditions, processes that control nocturnal convections, and the transition from shallow to deep convection on a diurnal time-scale. Long-term simulations are performed over two continental land sites: the Southern Great Plains (SGP) in the USA for 12 summer months from 2004 to 2015 and the Manacapuru site at the central Amazon (MAO) in Brazil for two full years from 2014 to 2015. The analysis is done on two regimes: afternoon convective regime and nocturnal precipitation regime. Most models produce afternoon precipitation too early, likely due to the missing transition of shallow-to-deep convection in these models. At SGP, the unified convection schemes better simulate the onset time of precipitation.At MAO, models produce the heating peak in a much lower level compared with observation, indicating too shallow convection in the models. For nocturnal precipitation, models that produce most of nocturnal precipitation all allow convection to be triggered above the boundary layer. This indicates the importance of model capability to detect elevated convection for simulating nocturnal precipitation. Sensitivity studies indicate that (a) nudging environmental variables towards observations has a minor impact on DCP, (b) unified treatment of shallow and deep convection and the capability to capture mid-level convection can help models better capture DCP, and (c) the interactions of the atmosphere with other components in the climate system (e.g. land) are also important for DCP simulations in coupled models. These results provide long-term statistical

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The impact of revised simplified Arakawa‐Schubert (RSAS) convective parameterization scheme in Climate Forecast System (CFS) version 2 (CFSv2) on the simulation of active and break phases of Indian summer monsoon (ISM) has been investigated. The results revealed that RSAS showed better fidelity in simulating monsoon features from diurnal to daily scales during active and break periods as compared to SAS simulation. Prominent improvement can be noted in simulating diurnal phase of precipitation in RSAS over central India (CI) and equatorial Indian Ocean (EIO) region during active periods. The spatial distribution of precipitation largely improved in RSAS simulation during active and break episodes. CFSv2 with SAS simulation has noticeable dry bias over CI and wet bias over EIO region which appeared to be largely reduced in RSAS simulation during both phases of the intraseasonal oscillation (ISO). During active periods, RSAS simulates more realistic probability distribution function (PDF) in good agreement with the observation. The relative improvement has been identified in outgoing longwave radiation, monsoon circulations, and vertical velocities in RSAS over SAS simulation. The improvement of rainfall distribution appears to be contributed by proper simulation of convective rainfall in RSAS. CFSv2 with RSAS simulation is able to simulate observed diurnal cycle of rainfall over CI. It correctly reproduces the time of maximum rainfall over CI. It is found that the improved feedback between moisture and convective processes in RSAS may be attributed to its improved simulation. Besides improvement, RSAS could not reproduce proper tropospheric temperature, cloud hydrometeors over ISM domain which shows the scope for future development.