Although biophysical yield responses to local warming have been studied, we know little about how crop yield growth—a function of climate and technology—responds to global temperature and socioeconomic changes. Here, we present the yield growth of major crops under warming conditions from preindustrial levels as simulated by a global gridded crop model. The results revealed that global mean yields of maize and soybean will stagnate with warming even when agronomic adjustments are considered. This trend is consistent across socioeconomic assumptions. Low-income countries located at low latitudes will benefit from intensive mitigation and from associated limited warming trends (1.8 °C), thus preventing maize, soybean and wheat yield stagnation. Rice yields in these countries can improve under more aggressive warming trends. The yield growth of maize and soybean crops in high-income countries located at mid and high latitudes will stagnate, whereas that of rice and wheat will not. Our findings underpin the importance of ambitious climate mitigation targets for sustaining yield growth worldwide.

No abstract

Droughts represent an important type of climate extreme that reduces crop production and food security. Although this fact is well known, the global geographic pattern of drought-driven reductions in crop production is poorly characterized. As the incidence of relatively more severe droughts is expected to increase under climate change, understanding the vulnerability of crop production to droughts is a key research priority. Here, we estimate the production losses of maize, rice, soy, and wheat from 1983 to 2009 using empirical relationships among crop yields, a drought index, and annual precipitation. We find that approximately three-fourths of the global harvested areas—454 million hectares—experienced drought-induced yield losses over this period, and the cumulative production losses correspond to 166 billion U.S. dollars. Globally averaged, one drought event decreases agricultural gross domestic production by 0.8%, with varying magnitudes of impacts by country. Crop production systems display decreased vulnerability or increased resilience to drought according to increases in per capita gross domestic production (GDP) in the countries with extensive semiarid agricultural areas. These changes in vulnerability accompany technological improvements represented by per capita GDP increases. Our estimates of drought-induced economic losses in agricultural systems offer a sound basis for subsequent assessments of the costs of adaptation to droughts under climate change.

[1] In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation ≥1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation ≥90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation ≥90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate than the dynamical models in the temporal variation pattern due to the strong dependence of the predictand on the relatively less accurate predictor (daily reanalysis precipitation); and (6) the simple statistical model is less plausible in the physical sense because of the oversimplification of underlying physical processes compared to the dynamical models and more sophisticated statistical models.

The use of different bias‐correction methods and global retrospective meteorological forcing data sets as the reference climatology in the bias correction of general circulation model (GCM) daily data is a known source of uncertainty in projected climate extremes and their impacts. Despite their importance, limited attention has been given to these uncertainty sources. We compare 27 projected temperature and precipitation indices over 22 regions of the world (including the global land area) in the near (2021–2060) and distant future (2061–2100), calculated using four Representative Concentration Pathways (RCPs), five GCMs, two bias‐correction methods, and three reference forcing data sets. To widen the variety of forcing data sets, we developed a new forcing data set, S14FD, and incorporated it into this study. The results show that S14FD is more accurate than other forcing data sets in representing the observed temperature and precipitation extremes in recent decades (1961–2000 and 1979–2008). The use of different bias‐correction methods and forcing data sets contributes more to the total uncertainty in the projected precipitation index values in both the near and distant future than the use of different GCMs and RCPs. However, GCM appears to be the most dominant uncertainty source for projected temperature index values in the near future, and RCP is the most dominant source in the distant future. Our findings encourage climate risk assessments, especially those related to precipitation extremes, to employ multiple bias‐correction methods and forcing data sets in addition to using different GCMs and RCPs.