This study quantifies the projected impacts of climate change on crop yields across temperature rise regimes and climatic zones, using the latest global dataset of site-level process-model simulations of crop responses to climate scenarios. We employed a threshold regression technique to identify and estimate temperature change thresholds and used linear mixed-effects models to assess the climate impacts on crop yields across different levels of temperature rise. The results indicated that warmer temperatures are detrimental to crop yields across countries, with negative impacts exacerbated when temperature increase exceeds threshold values. For instance, for wheat, a 1 °C temperature increase would result in a 6.1% yield loss when the temperature rise is below 2.38 °C; however, when it exceeds 2.38 °C, yield loss would rise to 8.2% per 1 °C warming. Similarly, the loss in rice yields for each °C increase in temperature would increase from 1.1 to 7.1% per °C when the temperature rise surpasses the 3.13 °C threshold. For maize, no threshold effect is found; instead, temperature increase would reduce yields by an average of 4.03% per °C. We also conducted impact assessments by climate zone, categorizing studied sites according to the Köppen climate classification system. We found that crop yields in arid regions are most adversely affected by global warming compared to other zones, while adaptive potential is higher for rice and wheat in temperate zones and for maize in continental zones. This study highlights the existence of threshold effects of temperature rise on crop yields and the varying yield impacts among climate zones, informing effective adaptation strategies to enhance global food security.

Climate change is regarded as one of the major factors enhancing the transmission intensity of dengue fever. In this study, we estimated the threshold effects of temperature on Aedes mosquito larval index as an early warning tool for dengue prevention. We also investigated the relationship between dengue vector index and dengue epidemics in Taiwan using weekly panel data for 17 counties from January 2012 to May 2019. To achieve our goals, we first applied the panel threshold regression technique to test for threshold effects and determine critical temperature values. Data were then further decomposed into different sets corresponding to different temperature regimes. Finally, negative binomial regression models were applied to assess the non-linear relationship between meteorological factors and Breteau index (BI). At the national level, we found that a 1°C temperature increase caused the expected value of BI to increase by 0.09 units when the temperature is less than 27.21 °C, and by 0.26 units when the temperature is greater than 27.21 °C. At the regional level, the dengue vector index was more sensitive to temperature changes because double threshold effects were found in the southern Taiwan model. For southern Taiwan, as the temperature increased by 1°C, the expected value of BI increased by 0.29, 0.63, and 1.49 units when the average temperature was less than 27.27 °C, between 27.27 and 30.17 °C, and higher than 30.17 °C, respectively. In addition, the effects of precipitation and relative humidity on BI became stronger when the average temperature exceeded the thresholds. Regarding the impacts of climate change on BI, our results showed that the potential effects on BI range from 3.5 to 54.42% under alternative temperature scenarios. By combining threshold regression techniques with count data regression models, this study provides evidence of threshold effects between climate factors and the dengue vector index. The proposed threshold of temperature could be incorporated into the implementation of public health measures and risk prediction to prevent and control dengue fever in the future.