Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that anthropogenic climate change has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 9 years of productivity growth. The effect is substantially more severe (a reduction of ~30-33%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.

The North American monsoon is a major focus of modern and paleoclimate research, but relatively little is known about interannual‐ to decadal‐scale monsoon moisture variability in the pre‐instrumental era. This study draws from a new network of subannual tree‐ring latewood width chronologies and presents a 470‐year reconstruction of monsoon (June–August) standardized precipitation for southwestern North America. Comparison with an independent reconstruction of cool‐season (October–April) standardized precipitation indicates that southwestern decadal droughts of the last five centuries were characterized not only by cool‐season precipitation deficits but also by concurrent failure of the summer monsoon. Monsoon drought events identified in the past were more severe and persistent than any of the instrumental era. The relationship between winter and summer precipitation is weak, at best, and not time stable. Years with opposing‐sign seasonal precipitation anomalies, as noted by other studies, were anomalously frequent during the mid to late 20th century.

Understanding the climatic drivers of present-day agricultural yields is critical for prioritizing adaptation strategies to climate change. However, unpacking the contribution of different environmental stressors remains elusive in large-scale observational settings in part because of the lack of an extensive long-term network of soil moisture measurements and the common seasonal concurrence of droughts and heat waves. In this study, we link state-of-the-art land surface model data and finescale weather information with a long panel of county-level yields for six major US crops to unpack their historical and future climatic drivers. To this end, we develop a statistical approach that flexibly characterizes the distinct intra-seasonal yield sensitivities to high-frequency fluctuations of soil moisture and temperature. In contrast with previous statistical evidence, we directly elicit an important role of water stress in explaining historical yields. However, our models project the direct effect of temperature-which we interpret as heat stress-remains the primary climatic driver of future yields under climate change.

The western United States was affected by several megadroughts during the last 1200 years, most prominently during the Medieval Climate Anomaly (MCA; 800 to 1300 CE). A null hypothesis is developed to test the possibility that, given a sufficiently long period of time, these events are inevitable and occur purely as a consequence of internal climate variability. The null distribution of this hypothesis is populated by a linear inverse model (LIM) constructed from global sea surface temperature anomalies and self-calibrated Palmer drought severity index data for North America. Despite being trained only on seasonal data from the late twentieth century, the LIM produces megadroughts that are comparable in their duration, spatial scale, and magnitude to the most severe events of the last 12 centuries. The null hypothesis therefore cannot be rejected with much confidence when considering these features of megadrought, meaning that similar events are possible today, even without any changes to boundary conditions. In contrast, the observed clustering of megadroughts in the MCA, as well as the change in mean hydroclimate between the MCA and the 1500–2000 period, are more likely to have been caused by either external forcing or by internal climate variability not well sampled during the latter half of the twentieth century. Finally, the results demonstrate that the LIM is a viable tool for determining whether paleoclimate reconstructions events should be ascribed to external forcings or to “out of sample” climate mechanisms, or if they are consistent with the variability observed during the recent period.

Global climate models are challenged to represent the North American monsoon, in terms of its climatology and interannual variability. To investigate whether a regional atmospheric model can improve warm season forecasts in North America, a retrospective Climate Forecast System (CFS) model reforecast (1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) and the corresponding NCEP-NCAR reanalysis are dynamically downscaled with the Weather Research and Forecasting model (WRF), with similar parameterization options as used for highresolution numerical weather prediction and a new spectral nudging capability. The regional model improves the climatological representation of monsoon precipitation because of its more realistic representation of the diurnal cycle of convection. However, it is challenged to capture organized, propagating convection at a distance from terrain, regardless of the boundary forcing data used. Dynamical downscaling of CFS generally yields modest improvement in surface temperature and precipitation anomaly correlations in those regions where it is already positive in the global model. For the North American monsoon region, WRF adds value to the seasonally forecast temperature only in early summer and does not add value to the seasonally forecast precipitation. CFS has a greater ability to represent the large-scale atmospheric circulation in early summer because of the influence of Pacific SST forcing. The temperature and precipitation anomaly correlations in both the global and regional model are thus relatively higher in early summer than late summer. As the dominant modes of early warm season precipitation are better represented in the regional model, given reasonable large-scale atmospheric forcing, dynamical downscaling will add value to warm season seasonal forecasts. CFS performance appears to be inconsistent in this regard.