Over India, heat waves occur during the summer months of April to June. A gridded daily temperature data set for the period, 1961–2013 has been analyzed to examine the variability and trends in heat waves over India. For identifying heat waves, the Excess Heat Factor (EHF) and 90th percentile of maximum temperatures were used. Over central and northwestern parts of the country, frequency, total duration and maximum duration of heat waves are increasing. Anomalous persistent high with anti-cyclonic flow, supplemented with clear skies and depleted soil moisture are primarily responsible for the occurrence of heat waves over India. Variability of heat waves over India is influenced by both the tropical Indian Ocean and central Pacific SST anomalies. The warming of the tropical Indian Ocean and more frequent El Nino events in future may further lead to more frequent and longer lasting heat waves over India.

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Heat wave has become a great concern for India in the recent years due to its disastrous impact on various sectors including health. Thus, accurate forecasts of heat wave events well in advance are required for preparing adequate mitigation strategies. The present study assesses the prediction skill of numerical weather prediction models in The Observing system Research and Predictability Experiment Interactive Grand Global Ensemble (TIGGE) experiments for predicting heat waves over India up to 7 days in advance. The models considered for this analysis are; the European Centre for Medium‐Range Weather Forecasts (ECMWF), the UK Met Office (UKMO) and National Centre for Environmental Prediction (NCEP). The model forecast verifications have been carried out for the hot weather season (April–June) over India for the period of 2008–2013. Fourteen heat wave events were identified during the study period using gridded daily maximum temperature (Tmax). The study reveals that the spatial distribution of maximum Temperature is well predicted by the TIGGE models for a forecast lead time of 1–7 days. The analysis suggested that the magnitude of heat wave events, even with a 7 days lead time, can be correctly predicted by more than 80% of ensemble members in all the TIGGE models. The prediction skill of the maximum temperatures over heat wave prone area during heat wave events is higher for ECMWF, then UKMO and NCEP models. The forecast verification analysis thus indicates that the TIGGE models are able to provide early warnings of heat waves with at least 5 days lead time.