Global climate change is intensifying and extreme weather events are occurring frequently, with far-reaching impacts on agricultural production. The Songnen Plain, as an important maize production region in China, faces challenges posed by climate change. This study aims to explore the effects of climate extremes on maize yield and provide a scientific basis for the adaptation of agriculture to climate change in this region. The study focuses on the spatial and temporal evolution characteristics of climate extremes during the maize reproductive period from 1988 to 2020 in the Songnen Plain and their impacts on maize yield. Daily temperature and precipitation data from 11 meteorological stations were selected and combined with maize yield information to assess the spatial and temporal trends of extreme climate indices using statistical methods such as the moving average and Mann–Kendall (M-K) mutation test. Pearson correlation analysis and a random forest algorithm were also used to quantify the degree of influence of extreme climate on maize yield. The results showed that (1) the extreme heat and humidity indices (TN90p, TX90p, CWD, R95p, R10, and SDII) tended to increase, while the cold indices (TN10p, TX10p) and the drought indices (CDD) showed a decreasing trend, suggesting that the climate of the Songnen Plain region tends to be warmer and more humid. (2) The cold indices in the extreme temperature indices showed a spatial pattern of being higher in the north and lower in the south and lower in the west and higher in the east, while the warm indices were the opposite, and the extreme precipitation indices showed a spatial pattern of being higher in the east and lower in the west. (3). Both maize yield and trend yield showed a significant upward trend. Maize meteorological yield showed a fluctuating downward trend within the range of −1.64~0.79 t/hm². During the 33 years, there were three climatic abundance years, two climatic failure years, and the rest of the years were normal years. (4) The cold index TN10p and warm indices TN90p and CWD were significantly correlated with maize yield, in which TN90p had the highest degree of positive correlation with yield, and in the comprehensive analysis, the importance of extreme climatic events on maize yield was in the order of TN90p, TN10p, and CWD. This study demonstrates the impact of extreme climate indices on maize yield in the Songnen Plain, providing a scientific basis for local agricultural management and decision-making, helping to formulate response strategies to mitigate the negative effects of extreme climate, ensure food security, and promote sustainable agricultural development.

With global warming driving an increase in extreme precipitation, the ensuing disasters present an unsustainable scenario for humanity. Consequently, understanding the characteristics of extreme precipitation has become paramount. Analyzing observational data from 1961 to 2020 across 29 meteorological stations in Heilongjiang Province, China, we employed kriging interpolation, the trend-free pre-whitening Mann–Kendall (TFPW–MK) method, and linear trend analysis. These methods allowed us to effectively assess the spatiotemporal features of extreme precipitation. Furthermore, Pearson’s correlation analysis explored the relationship between extreme precipitation indices (EPIs) and geographic factors, while the geodetector quantified the impacts of climate teleconnections. The results revealed the following: (1) There has been a clear trend in increasing extreme precipitation over the last few decades, particularly in the indices of wet day precipitation (PRCPTOT), very wet day precipitation (R95P), and extremely wet day precipitation (R99P), with regional mean trends of 10.4 mm/decade, 5.7 mm/decade, and 3.4 mm/decade, respectively. This spatial trend showed a decrease from south to north. (2) Significant upward trends were observed in both spring and winter for the maximum 1-day precipitation (RX1day) and the maximum 5-day precipitation (RX5day). (3) The latitude and longitude were significantly correlated with the most extreme precipitation indices, while elevation showed a weaker correlation. (4) Extreme precipitation exhibited a nonlinear response to large-scale climate teleconnections, with the combined influence of factors having a greater impact than individual factors. This research provides critical insights into the spatiotemporal dynamics of extreme precipitation, guiding the development of targeted strategies to mitigate risks and enhance resilience. It offers essential support for addressing regional climate challenges and promoting agricultural development in Heilongjiang Province.

Understanding the spatial patterns and driving mechanisms of net primary productivity (NPP) and precipitation utilization efficiency (PUE) is crucial for assessing ecosystem services. This study analyzed the variations in NPP and PUE in Heilongjiang Province from 2001 to 2020, using MOD17A3 NPP products and meteorological, topographic, and land use data. The distribution of the NPP and PUE of seven land use categories was determined in the study, namely, cropland, forest, grassland, water, barren, impervious and wetland. The multi-year spatial averages for NPP and PUE were 428.96 gC·m⁻²·a⁻¹ and 0.74 gC·m⁻²·mm⁻¹, respectively, with forests showing the highest values and barren lands the lowest. During the study period, 91.4% of the NPP increased at an average rate of 3.36 gC·m⁻²·a⁻¹, while PUE exhibited a polarized trend. Changes in land use, especially conversions involving cropland and forest, along with climatic factors such as rising precipitation and temperature, significantly influenced NPP and PUE dynamics. These findings provide a scientific basis for ecological restoration and the assessment of ecosystem function under changing climatic conditions.

Precipitation unevenness significantly influences the rational allocation of water resources and the management of agricultural irrigation. Based on precipitation data from 29 meteorological stations in Heilongjiang Province, China, from 1961 to 2020, this study calculated the precipitation concentration index (PCI), precipitation concentration degree (PCD), and precipitation concentration period (PCP) to analyze the spatial distribution characteristics of precipitation heterogeneity at three distinct timescales: year, maize growth period, and the four stages of the maize growth period. The findings reveal that the rainy season in Heilongjiang Province commences earlier in the southwest compared with the northeast and northwest, with a primary concentration in July. At the annual scale, PCI in southwestern Heilongjiang Province surpasses that in the southeastern region, displaying an approximate east–west gradient in PCD and PCP values ranging from 0.544 to 0.746 and 196 to 203, respectively. During the growth period scale, precipitation concentrates in the southwest and central regions, occurring earlier than in the northeast and northwest. In contrast to the annual scale, the PCI value is smaller, and precipitation predominantly concentrates in mid and late July. Examining the four stages of the maize growth period, PCD generally exhibits a decreasing gradient from west to east. The highest values of PCI and PCD manifest in the southwestern part of Heilongjiang Province, with precipitation concentrated in the middle of each growth stage. The research results serve as a valuable reference for policymakers and stakeholders involved in water resource allocation and agricultural water management in Heilongjiang Province.