Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China’s vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland region), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited the highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET (evapotranspiration) and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.

The S-locus-specific glycoprotein/EP1 gene family encodes a class of epidermis-specific secretory glycoproteins involved in plant responses to abiotic stress. However, their role in cotton (Gossypium hirsutum) drought response remains unclear. Here, we identified 12 GhEP1 genes from the G. hirsutum genome, clustered into three subfamilies. Structural predictions indicated that most GhEP1 proteins are hydrophilic and localised to the outer side of the plasma membrane or nucleus, with a few containing transmembrane domains. Collinearity analysis revealed that segmental and tandem duplications drove family expansion, with a high degree of collinearity conservation among dicotyledonous plants. Transcriptomic data showed generally low GhEP1 expression across tissues, but GhEP1-2, GhEP1-6, GhEP1-7 and GhEP1-8 responded significantly to NaCl, PEG-induced drought and cold stresses. qRT-PCR further confirmed that the expression of most GhEP1 genes was significantly up-regulated in roots under drought stress, while GhEP1-3 was induced in leaves, indicating tissue-specific regulatory divergence. This study systematically characterises the GhEP1 family and provides potential genetic resources for improving drought tolerance in cotton.

Global Climate Models (GCMs) are a primary source of uncertainty in assessing climate change impacts on agricultural production, especially when relying on limited models. Considering China's vast territory and diverse climates, this study utilized 22 GCMs and selected three representative cotton-producing regions: Aral (northwest inland), Wangdu (Yellow River basin), and Changde (Yangtze River basin). Using the APSIM model, we simulated climate change effects on cotton yield, water consumption, uncertainties, and climatic factor contributions. Results showed significant variability driven by different GCMs, with uncertainty increasing over time and under radiation forcing. Spatial variations in uncertainty were observed: Wangdu exhibited highest uncertainties in yield and phenology, while Changde had the greatest uncertainties in ET and irrigation amount. Key factors affecting yield varied regionally—daily maximum temperature and precipitation dominated in Aral; precipitation was a major negative factor in Wangdu; and maximum temperature and solar radiation were critical in Changde. This study provides scientific support for developing climate change adaptation measures tailored to cotton production across different regions.