plausible scenario for assessment of risk for the insurance sector. Clim. Risk Manag. 13, 1–9 (2016).
 30. Jägermeyr, J. & Frieler, K. Spatial variations in crop growing seasons pivotal to reproduce global fluctuations in maize and wheat 
yields. Sci. Adv. 4(11), eaat4517 (2018).
 31. Elliott, J. et al. Characterizing agricultural impacts of recent large-scale US droughts and changing technology and management. 
Agric. Syst. 159, 275–281 (2017).
 32. Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl. Acad. Sci. 112, 6931–6936 (2015).
 33. Tao, F ., Zhang, Z., Liu, J. & Y okozawa, M. Modelling the impacts of weather and climate variability on crop productivity over a 
large area: A new super-ensemblebased probabilistic projection. Agric. For. Meteorol. 149, 1266–1278 (2009).
 34. Parent, B. et al. Maize yields over Europe may increase in spite of climate change, with an appropriate use of the genetic variability 
of flowering time. PNAS 115(42), 10642–10647 (2018).
 35. Y ang, C. Y ., Fraga, H., Ieperen, W . V . & Santos, J. A. Assessment of irrigated maize yield response to climate change scenarios in 
Portugal. Agric. Water Manag. 184, 178–190 (2017).
 36. Miller, S. A. & Moore, F . C. Climate and health damages from global concrete production. Nat. Clim. Change https:// doi. org/ 10. 
1038/ s41558- 020- 0733-0 (2020).
 37. Kassie, B. T. et al. Exploring climate change impacts and adaptation options for maize production in the Central Rift Valley of 
Ethiopia using different climate change scenarios and crop models. Clim. Change 129, 145–158 (2015).
 38. Tao, F . & Zhang, Z. Climate change, high-temperature stress, rice productivity, and water use in Eastern China: A new superensem-
ble-based probabilistic projection. J. Appl. Meteorol. Climatol. 52, 531–551 (2013).
 39. Glotter, M. & Elliott, J. Simulating US agriculture in a modern Dust Bowl drought. Nat. Plants 3, 16193 (2016).
 40. Challinor, A. J., Koehler, A. K., Ramirez-Villegas, J., Whitfield, S. & Das, B. Current warming will reduce yields unless maize 
breeding and seed systems adapt immediately. Nat. Clim. Change 6, 954–958 (2016).
 41. Cammarano, D. et al. Using historical climate observations to understand future climate change crop yield impacts in the South-
eastern US. Clim. Change 134, 311–326 (2016).
 42. Etten, J. V . et al. Crop variety management for climate adaptation supported by citizen science. PNAS 116(10), 4194–4199 (2019).
 43. Urban, D. W ., Sheffield, J. & Lobell, D. B. The impacts of future climate and carbon dioxide changes on the average and variability 
of US maize yields under two emission scenarios. Environ. Res. Lett. 10, 045003 (2015).
 44. IPCC. Summary for policymakers. In Global Warming of 1.5°C. An IPCC Special Report on the Impacts of Global Warming of 1.5°C 
Above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global 
Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty 32 (World Meteorological 
Organization, 2018).
 45. Ruane, A. C., Goldberg, R. & Chryssanthacopoulos, J. Climate forcing datasets for agricultural modeling: Merged products for 
gap-filling and historical climate series estimation. Agr. For. Meteorol. 200, 233–248 (2015).
 46. Hempel, S., Frieler, K., Warszawski, L., Schewe, J. & Piontek, F . A trendpreserving bias correction-the ISI-MIP approach. Earth 
Syst. Dyn. 4, 219–236 (2013).
 47. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological 
types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, 1022 (2008).
 48. Y ou, L.Z., et al. Spatial Production Allocation Model (SPAM) 2000 Version 3.2. http:// mapsp am. info (2015).
 49. Hoogenboom, G., et al. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.6 (DSSAT Foundation, 2015).