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During the recent decade, with the growing recognition of the possibility of climate change and clear evidence of observed changes in climate during 20 th century, an increasing emphasis on food security and its regional impacts has come to forefront of the scientific community. In recent times, the crop simulation models have been used extensively to study the impact of climate change on agricultural production and food security. The output provided by the simulation models can be used to make appropriate crop management decisions and to provide farmers and others with alternative options for their farming system. It is expected that in the coming decades with the increased use of computers, the use of simulation models by farmers and professionals as well as policy and decision makers will increase. In India, substantial work has been done in last decade aimed at understanding the nature and magnitude of change in yield of different crops due to projected climate change. This paper presents an overview of the state of the knowledge of possible effect of the climate variability and change on food grain production in India.

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In spite of the summer monsoon’s importance in determining the life and economy of an agriculture-dependent country like India, committed efforts toward improving its prediction and simulation have been limited. Hence, a focused mission mode program Monsoon Mission (MM) was founded in 2012 to spur progress in this direction. This article explains the efforts made by the Earth System Science Organization (ESSO), Ministry of Earth Sciences (MoES), Government of India, in implementing MM to develop a dynamical prediction framework to improve monsoon prediction. Climate Forecast System, version 2 (CFSv2), and the Met Office Unified Model (UM) were chosen as the base models. The efforts in this program have resulted in 1) unparalleled skill of 0.63 for seasonal prediction of the Indian monsoon (for the period 1981–2010) in a high-resolution (∼38 km) seasonal prediction system, relative to present-generation seasonal prediction models; 2) extended-range predictions by a CFS-based grand multimodel ensemble (MME) prediction system; and 3) a gain of 2-day lead time from very high-resolution (12.5 km) Global Forecast System (GFS)-based short-range predictions up to 10 days. These prediction skills are on par with other global leading weather and climate centers, and are better in some areas. Several developmental activities like coupled data assimilation, changes in convective parameterization, cloud microphysics schemes, and parameterization of land surface processes (including snow and sea ice) led to the improvements such as reducing the strong model biases in the Indian summer monsoon simulation and elsewhere in the tropics.