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Drought events have critical impacts on agricultural production yet there is little consensus on how these should be measured and defined, with implications for drought research and policy. We develop a flexible rainfall-temperature drought index that captures all dry events and we classify these as Type 1 (above-average cooling degree days) and Type 2 droughts (below-average cooling degree days). Applied to a panel dataset of Indian districts over 1966–2009, Type 2 droughts are found to have negative marginal impacts comparable to those of Type 1 droughts. Irrigation more effectively reduces Type 2 drought-induced yield losses than Type 1 yield losses. Over time, Type 1 drought losses have declined while Type 2 losses have risen. Estimates of average yield losses due to Type 1 droughts are reduced by up to 27 per cent when Type 2 droughts are omitted. The associated ex-post economic costs in terms of rice production are underestimated by up to 124 per cent.

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Climate change is driving a rise in the intensity and frequency of extreme weather events. Such events are characterised as thresholds beyond which cereal yields significantly change. We apply a threshold model to district-level data collected in India over 1966-2011 and objectively identify thresholds, measured by the Standardised Precipitation-Evapotranspiration Index, before estimating their yield effects, for rice, wheat, maize, millet, sorghum and barley. Heterogeneous, crop-specific thresholds are identified for all crops except wheat. Thresholds are identified at normal climatic conditions but have smaller negative marginal effects than those of thresholds identified at dry conditions. The extent to which agro-ecological conditions and irrigation influence the location of thresholds and the size of their marginal effects varies by crop. Thresholds identified at dry climatic conditions severely reduce yield yet are rarely crossed; those at normal conditions moderately affect yield but are frequently crossed. A threshold's total impact on production is found to be inverse to the severity of its marginal effect. Severe-effect thresholds have been crossed with increasing frequency over time, contributing to growth in the size of total impacts. Our results have welfare implications and have the potential to inform predictions about the impacts of extreme weather events.