High-resolution climate prediction in mountainous terrain using a ConvLSTM-XGBoost hybrid model with dynamic bayesian weighting

TL;DR

A new hybrid model combining ConvLSTM and XGBoost improves climate predictions in mountainous regions by capturing spatiotemporal patterns and topographic effects.

Contribution

The model introduces dynamic Bayesian weighting to adaptively calibrate ConvLSTM and XGBoost components for improved climate prediction in complex terrain.

Findings

The hybrid model reduced precipitation prediction MAE by 30.5% compared to CMIP6.

It improved the F1-score for extreme precipitation identification by 20%.

The model achieved 96.53% accuracy in maximum temperature predictions with low error.

Abstract

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.

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