To address the challenge where the interplay between spatiotemporal dynamics and topographic effects complicates climate modeling over complex terrain, we propose a hybrid ConvLSTM-XGBoost model incorporating dynamic Bayesian weighting, and demonstrate its capacity for high-precision climate prediction through a case study in the Hongyuan Mountain region of Yunnan, China (22.5°–23.5°N, 102.5°–103.5°E); specifically, the ConvLSTM network captures spatiotemporal evolution patterns (e.g., propagation of the southwest monsoon front) from the 0.25° resolution CN05.1 climate dataset, while XGBoost quantifies the nonlinear modulation effects of 90-m SRTM DEM-derived topographic features (elevation, aspect) on precipitation phases, with an innovatively integrated Bayesian Model Averaging (BMA) framework dynamically calibrating model weights—XGBoost at 0.68 ± 0.05 during dry seasons and ConvLSTM at 0.72 ± 0.07 during monsoons—to enhance responsiveness to extreme events. Validation using 1961–2022 climate data shows the hybrid model reduces precipitation prediction mean absolute error (MAE) by 30.5% compared to CMIP6 (achieving an MAE of 0.0089 [specify units, e.g., mm/day]), improves the F1-score for identifying extreme precipitation (> 50 mm/day) by 20%, achieves 96.53% accuracy in maximum temperature (Tmax) predictions (errors ≤ 3%), and reduces high-temperature dispersion by 52%, thereby serving as a 1-km resolution decision-support tool for mountain climate risk management, supporting drought warning and hydropower scheduling in Yunnan’s Climate Adaptation Plan 2035, and offering a scalable framework for global mountain climate modeling.

Sharing scientific data is an effective means to rationally exploit scientific data and is vital to promote the development of the industrial chain and improve the level of science and technology. In recent years, the popularity of the open data platform has increased, but problems remain, including imperfect system architecture, unsound privacy and security, and non-standardized interaction data. To address these problems, the blockchain’s decentralization, smart contracts, distributed storage, and other features can be used as the core technology for open data systems. This paper addresses the problems of opening, allocation-right confirmation, sharing, and rational use of wild-bird data from Yunnan Province, China. A data storage model is proposed based on the blockchain and interstellar file system and is applied to wild-bird data to overcome the mutual distrust between ornithology institutions in the collaborative processing and data storage of bird data. The model provides secure storage and secure access control of bird data in the cloud, thereby ensuring the decentralized and secure storage of wild-bird data for multiple research institutions.