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While there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions. [ABSTRACT FROM AUTHOR]

We assess the skill of the fully coupled lagged ensemble forecasts from GloSea5‐GC2, for the sub‐seasonal to seasonal (S2S) timescale up to 4 weeks, with the aim of understanding how these forecasts might be used in a Ready‐Set‐Go style decision‐making framework. Integrated Multi‐satellite Retrievals for Global Precipitation Measurement (IMERG‐GPM) are used to seamlessly verify these ensemble forecasts up to monthly timescales whereby forecast and observed precipitation fields are summed over a sequence of increasing lead time accumulation windows (LTAWs), from 1d1d up to 2w2w. Results show that model biases grow with increasing LTAW and with ensemble member age. The S2S model exhibits both wet and dry biases across different parts of the Indian domain. The S2S model error grows from around 10 mm for a 24‐h accumulation to 50 mm for the 2‐week LTAWs. The actual skill and potential skill of the ensemble forecasts reveal that the potential skill is not always greater than actual skill everywhere. The sensitivity to the number and age of ensemble members was tested, with potential skill showing more impact from the exclusion of older members at all LTAWs. We conclude that the older lagged members do not necessarily add value by being included in the short to medium range or even for the extended range forecasts. GloSea5‐GC2 shows some skill in detecting large accumulations, which are not always tied to locations where they are climatologically frequent. [ABSTRACT FROM AUTHOR]

An extended range prediction system (NERP) based on the Unified global coupled modelling system has been implemented at National Centre for Medium Range Weather Forecasting (NCMRWF). Predictions of weekly anomalies for rainfall, winds and surface temperature are issued once a week for the subsequent 4 weeks. This study is an assessment of the skill of the NERP system over India during the summer monsoon period of June to September (JJAS). The week‐1 and week‐2 rainfall anomaly forecasts exhibit good prediction skill over the Indian region. The evaluations made on meteorologically homogeneous regions of India show moderate to good skill even in week‐3 and week‐4 forecasts for some regions. The variability of the monsoon seasonal cycle in the model is well represented along with the magnitude of summer monsoon rainfall. The model brings out the mean monsoon rainfall features – the peaks during JJAS occurring over the Western Ghats, the Arakan coast and the Bay of Bengal. The onset of the monsoon and the interannual variability of the northward propagation of the rainfall anomalies over the Indian longitudes match reasonably well with observations. The model has some wet bias over the equatorial Indian Ocean and mild dry bias over the Indian land region that develop during the 4‐week period. The anomaly correlations of rainfall are significant over most of the subdivisions of India till week‐3 and week‐4. Probabilistic scores of reliability Receiver Operating Characteristic (ROC) and fair continuous ranked probability score (FCRPS) show that the model exhibits good skill over most regions till week‐2 and moderate skill in weeks 3–4 over other regions. The estimates of forecast reliability are low, a likely consequence of the limited ensemble size. A case‐study of year 2018 showed that the JJAS rainfall regime shifts from wet to dry and vice versa are captured well. [ABSTRACT FROM AUTHOR]