The contribution of factors including fuel type, fire-weather conditions, topography and human activity to fire regime attributes (e.g. fire occurrence, size distribution and severity) has been intensively discussed. The relative importance of those factors in explaining the burn probability (BP), which is critical in terms of fire risk management, has been insufficiently addressed. Focusing on a subtropical coniferous forest with strong human disturbance in East China, our main objective was to evaluate and compare the relative importance of fuel composition, topography, and human activity for fire occurrence, size and BP. Local BP distribution was derived with stochastic fire simulation approach using detailed historical fire data (1990–2010) and forest-resource survey results, based on which our factor contribution analysis was carried out. Our results indicated that fuel composition had the greatest relative importance in explaining fire occurrence and size, but human activity explained most of the variance in BP. This implies that the influence of human activity is amplified through the process of overlapping repeated ignition and spreading events. This result emphasizes the status of strong human disturbance in local fire processes. It further confirms the need for a holistic perspective on factor contribution to fire likelihood, rather than focusing on individual fire regime attributes, for the purpose of fire risk management.

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Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Crop phenology changes are important indicators of climate change. Climate change impacts on crop phenology are generally investigated through statistical analysis of the relationship between growth period length and growth period mean temperature. However, growth periods may be either earlier or later in a given year; hence, changes in mean temperature indicate both the effects of climate change and those attributable to seasonal temperature differences. Failure to consider temperature change resulting from seasonal shifts can lead to biased estimation of warming trends and their corresponding impact on phenology. We evaluated this potential bias in rice phenology change in 892 phenology series from China by applying time series regression control for phenological dates. The results indicate that the true magnitudes of climate change for early rice, late rice, and single rice are 0.20-0.56, 0.23-0.86, and 0.28-0.38 K/decade, after correction for the effects of seasonal shifts. The effects of seasonal shifts of growth periods led to underestimates of the magnitude of climate change by 0.16-0.22 and 0.05-0.08 K/decade for early rice and single rice, respectively, and an overestimate of the effect for late rice of 0.02-0.06 K/decade. Correspondingly, the net warming impacts on growth period length after correcting for the effects of seasonal shifts were − 2.7 d/K for early rice, − 4.8 d/K for late rice, and − 3.1 d/K for single rice, which were weaker for early and single rice, but stronger for late rice, relative to previous reports. Changes in growth period length were most closely associated with variation in phenological dates, while their relationship with climate change was less pronounced. Our results indicate that earlier phenological dates and prolonged-duration cultivars have been adopted to offset the impact of climate change, providing further evidence of active adaptation of rice cultivation practice to climate change in China.

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There has been increasing interest in understanding climate change impacts on crop yield stability, including interannual yield variability and lower yield extremes, in addition to mean yield. In this study, we evaluated these impacts on wheat yield and investigated the contribution of changes in climate mean and variability, and their interaction, on the North China Plain (NCP). Wheat yield simulation experiments with control groups were conducted using the Crop Environment Resource Synthesis (CERES) model, with multiple general circulation model ensembles under two representative concentration pathways (RCPs), namely 4.5 and 8.5. Climate change was projected to reduce mean yield by 15 and 17%, increase yield interannual variability by 5 and 11%, and reduce 10-year return period lower yield extremes by 31 and 34% under RCPs 4.5 and 8.5, respectively. When analysed, changes in climate mean proved the main cause for changes in mean yield (62-71%), followed by the interactive changes in climate mean and variability (26-33%). As for the impact on yield variability, the interaction of the changes in climate mean and variability proved the main cause (48-54%), followed by changes in climate mean (33-41%). Surprisingly, climate change in variability contributed the least in both cases. Our results pertaining to the decrease in both availability and stability of wheat yield on the NCP presents a greater challenge for building a resilient food system for local areas than before. They also highlighted the importance of separating the impacts of changes in climate mean and variability on crop yield stability in a holistic framework, with particular attention paid to the tangible and interactive effects. K E Y W O R D S changes in climate mean and variability, crop model, North China Plain, wheat yield stability