Increasing drought and extreme rainfall are major threats to maize production in the United States. However, compared to drought impact, the impact of excessive rainfall on crop yield remains unresolved. Here, we present observational evidence from crop yield and insurance data that excessive rainfall can reduce maize yield up to −34% (−17 ± 3% on average) in the United States relative to the expected yield from the long‐term trend, comparable to the up to −37% loss by extreme drought (−32 ± 2% on average) from 1981 to 2016. Drought consistently decreases maize yield due to water deficiency and concurrent heat, with greater yield loss for rainfed maize in wetter areas. Excessive rainfall can have either negative or positive impact on crop yield, and its sign varies regionally. Excessive rainfall decreases maize yield significantly in cooler areas in conjunction with poorly drained soils, and such yield loss gets exacerbated under the condition of high preseason soil water storage. Current process‐based crop models cannot capture the yield loss from excessive rainfall and overestimate yield under wet conditions. Our results highlight the need for improved understanding and modeling of the excessive rainfall impact on crop yield.

Heat waves (HWs) are among the most damaging climate extremes to human society. Climate models consistently project that HW frequency, severity, and duration will increase markedly over this century. For urban residents, the urban heat island (UHI) effect further exacerbates the heat stress resulting from HWs. Here we use a climate model to investigate the interactions between the UHI and HWs in 50 cities in the United States under current climate and future warming scenarios. We examine UHI 2m (defined as urban-rural difference in 2m-height air temperature) and UHI s (defined as urban-rural difference in radiative surface temperature). Our results show significant sensitivity of the interaction between UHI and HWs to local background climate and warming scenarios. Sensitivity also differs between daytime and nighttime. During daytime, cities in the temperate climate region show significant synergistic effects between UHI and HWs in current climate, with an average of 0.4 K higher UHI 2m or 2.8 K higher UHI s during HWs than during normal days. These synergistic effects, however, diminish in future warmer climates. In contrast, the daytime synergistic effects for cities in dry regions are insignificant in the current climate, but emerge in future climates. At night, the synergistic effects are similar across climate regions in the current climate, and are stronger in future climate scenarios. We use a biophysical factorization method to disentangle the mechanisms behind the interactions between UHI and HWs that explain the spatial-temporal patterns of the interactions. Results show that the difference in the increase of urban versus rural evaporation and enhanced anthropogenic heat emissions (air conditioning energy use) during HWs are key contributors to the synergistic effects during daytime. The contrast in water availability between urban and rural land plays an important role in determining the contribution of evaporation. At night, the enhanced release of stored and anthropogenic heat during HWs are the primary contributors to the synergistic effects.

Drought is one of the leading impediments to development in Africa. Much of the continent is dependent on rain-fed agriculture, which makes it particularly susceptible to climate variability. Monitoring drought and providing timely seasonal forecasts are essential for integrated drought risk reduction. Current approaches in developing regions have generally been limited, however, in part because of unreliable monitoring networks. Operational seasonal climate forecasts are also deficient and often reliant on statistical regressions, which are unable to provide detailed information relevant for drought assessment. However, the wealth of data from satellites and recent advancements in large-scale hydrological modeling and seasonal climate model predictions have enabled the development of state-of-the-art monitoring and prediction systems that can help address many of the problems inherent to developing regions. An experimental drought monitoring and forecast system for sub-Saharan Africa is described that is... Drought is one of the leading impediments to development in Africa. Much of the continent is dependent on rain-fed agriculture, which makes it particularly susceptible to climate variability. Monitoring drought and providing timely seasonal forecasts are essential for integrated drought risk reduction. Current approaches in developing regions have generally been limited, however, in part because of unreliable monitoring networks. Operational seasonal climate forecasts are also deficient and often reliant on statistical regressions, which are unable to provide detailed information relevant for drought assessment. However, the wealth of data from satellites and recent advancements in large-scale hydrological modeling and seasonal climate model predictions have enabled the development of state-of-the-art monitoring and prediction systems that can help address many of the problems inherent to developing regions. An experimental drought monitoring and forecast system for sub-Saharan Africa is described that is...