Agriculture (arguably the backbone of India's economy) is highly dependent on the spatial and temporal distribution of monsoon rainfall. This paper presents an analysis of crop-climate relationships for India, using historic production statistics for major crops (rice, wheat, sorghum, groundnut and sugarcane) and for aggregate food grain, cereal, pulses and oilseed production. Correlation analysis provides an indication of the influence of monsoon rainfall and some of its potential predictors (Pacific and Indian Ocean sea-surface temperatures, Darwin sea-level pressure) on crop production. All-India annual total production (except sorghum and sugarcane), and production in the monsoon (except sorghum) and post-monsoon seasons (except rice and sorghum) were significantly correlated to all-India summer monsoon rainfall. Monsoon season crops (except sorghum) were strongly associated with the three potential monsoon predictors. Results using state-level crop production statistics and subdivisional monsoon rainfall were generally consistent with the all-India results, but demonstrated some surprising spatial variations. Whereas the impact of subdivisional monsoon rainfall is strong in most of the country, the influence of concurrent predictors related to El Niño-southern oscillation and the Indian Ocean sea-surface temperatures at a long lead time seem greatest in the western to central peninsula.

The present study is an assessment of a two-member ensemble of transient climate change simulations, with a focus on the Indian summer monsoon and ENSO-monsoon teleconnection. The CNRM ocean-atmosphere coupled model is integrated from 1950 to 2099 and driven by changes in concentrations of greenhouse gases and sulfate aerosols. The simulated monsoon climate is first validated against available observations and NCEP/ NCAR reanalyses over the second half of the 20 th century. The model captures the main features of the Indian monsoon climate and the main mode of variability found in the tropical regions, namely the El Niñ o Southern Oscillation, reasonably well. During the second half of the 21 st century, both scenarios indicate a significant increase in the annual mean surface air temperature (about 2 C) and in monsoon precipitation (less than 10%) over India, relative to the 1950-1999 climatology. However, the model does not show a clear strengthening of the monsoon circulation, but rather a northward shift of the westerly monsoon flow. The increase in monsoon precipitation is therefore partly due to a 'non-dynamical' response to global warming, namely a large increase in precipitable water over India. While the transient response of the model shows a qualitative agreement with the surface warming observed over recent decades, neither the observations nor the model indicate significant trends in All India monsoon rainfall in the late 20 th century. A long-term increase in simulated monsoon precipitation does appear from 1950 to 2099, but is superimposed onto relatively large multi-decadal fluctuations. The simulated ENSO-monsoon teleconnection also shows a strong modulation on multi-decadal time scales, but no systematic change with increasing amounts of greenhouse gases.

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Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, 'cloudbursts', which are devastating convective phenomena producing sudden high-intensity rainfall (∼ 10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km gridresolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometers.

The Indian Monsoon Data Assimilation and Analysis (IMDAA) is a regional high‐resolution atmospheric reanalysis over the Indian subcontinent. This regional reanalysis over India is the first of its kind and is produced by the National Centre for Medium Range Weather Forecasting and Met Office, UK, in collaboration with the India Meteorological Department under the National Monsoon Mission project of the Ministry of Earth Sciences, Government of India. The reanalysis runs from 1979 to 2018, to span the era of modern meteorological satellites. This article briefly describes the IMDAA system and discusses the performance of the IMDAA during summer monsoon (June–September). This study provides evidence for substantial improvements seen in IMDAA compared to the ERA‐Interim reanalysis fields over India. The evaluation is carried out for the period of 1979–1993 for all major features associated with the Indian Monsoon to highlight improvements compared to ERA‐Interim and to document the biases. The study also demonstrates the potential use of the IMDAA data for applications such as wind resource assessment over India.

Performance of four mesoscale models namely, the MM5, ETA, RSM and WRF, run at NCMRWF for short range weather forecasting has been examined during monsoon-2006. Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind, temperature, specific humidity, geopotential height, rainfall, systematic errors, root mean square errors and specific events like the monsoon depressions.It is very difficult to address the question of which model performs best over the Indian region? An honest answer is 'none'. Perhaps an ensemble approach would be the best. However, if we must make a final verdict, it can be stated that in general, (i) the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and, the MM5 is able to produce best All India rainfall forecasts in day-3, but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India, (ii) the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time, and (iii) the RSM is able to produce least errors in the day-1 forecasts of the tracks, while the ETA model produces least errors in the day-3 forecasts.

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