Global warming and dimming/brightening have significant implications for crop systems and exhibit regional variations. It is important to clarify the changes in regional thermal and solar radiation resources and estimate the impacts on potential crop production spatially and temporally. Based on daily observation data during 1961–2015 in the North China Plain (NCP), the impacts of climate change associated with climate warming and global dimming/brightening on potential light–temperature productivity (PTP) of summer maize were assessed in this study. Results show that the NCP experienced a continuous warming and dimming trend in maize growing season during the past 55 years, and both ATT10 and solar radiation had an abrupt change in the mid-1990s. The period of 2000–2015 was warmer and dimmer than any other previous decade. Assuming the maize growing season remains unchanged, climate warming would increase PTP of summer maize by 4.6% over the period of 1961–2015, which mainly occurred in the start grain filling–maturity stage. On the other hand, as negative contribution value of solar radiation to the PTP was found in each stage, dimming would offset the increase of PTP due to warming climate, and lead to a 15.6% reduction in PTP in the past 55 years. This study reveals that the changes in thermal and solar radiation have reduced the PTP of summer maize in the NCP. However, the actual maize yield could benefit more from climate warming because solar radiation is not a limiting factor for the current low production level.

Soil salinization is projected to intensify under global warming, posing significant constraints on crop growth and agricultural productivity. Although numerous quantitative studies have investigated soil salinization, comprehensive assessments specifically targeting global croplands remain limited. We hypothesize that, within a machine learning framework, combining soil properties, climate variables and anthropogenic management factors can yield global maps of soil salinity in croplands. For this purpose, we use a random forest (RF) model, with irrigation involved, to predict global cropland soil salinity (ECe) at 0.1° resolution for 1981–2010, capturing its spatiotemporal dynamics. The results indicate that the model performs well (R² = 0.63), with soil depth, the aridity index and pH being particularly significant factors. High values of ECe were found across central South America, southwestern Africa, central India, and south-central and northeastern China. The proportion of salinized croplands exhibits a long-term upward trend, averaging 4.88%. Ultimately, this study delivers long-term global cropland salinity maps, offering critical insights for safeguarding food security under climate change.