AI 中文总结
研究针对区域气候预测中时间序列基础模型的挑战，提出残差引导多分辨率细化框架RGMR，无需更新主干参数，应用于干旱预测时能降低测试集MSE，为部署冻结的TSFM提供实用途径。
AI 中文摘要
区域气候预测对时间序列基础模型提出了独特挑战，其通常通过单遍推理处理时间模式。相比之下，气候专家采用多尺度时间分析和基于系统误差诊断的迭代细化。我们提出了RGMR（残差引导多分辨率细化），这是一个推理时框架，可使预训练基础模型在不更新主干参数的情况下进行结构化的从粗到细的细化以进行气候预测。应用于使用标准化降水蒸散指数（SPEI）的干旱预测时，RGMR在每个站点评估的三个TSFM主干（TimesFM、TimeGPT、TabPFN）上与架构无关，并持续降低了南澳大利亚三个站点和南澳大利亚以外其他三个地区的测试集MSE。应用于TimesFM时，该包装器在南澳大利亚的三个站点将提前一个月的SPEI MSE降低了高达18.9%（平均降低约18.7%）。总体而言，RGMR为在区域气候预测工作流程中部署冻结的TSFM提供了一条实用途径。
英文摘要
Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert climatologists, in contrast, employ multi-scale temporal analysis and iterative refinement based on systematic error diagnosis. We present RGMR (Residual-Guided Multi-Resolution Refinement), an inference-time framework that adapts pre-trained foundation models to perform structured coarse-to-fine refinement for climate forecasting without updating backbone parameters. Applied to drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI), RGMR is architecture-agnostic across the three TSFM backbones evaluated per site (TimesFM, TimeGPT, TabPFN) and consistently lowers test-set MSE on three South Australian sites and three additional regions outside South Australia. Applied to TimesFM, the wrapper reduces one-month-ahead SPEI MSE by up to 18.9\% across the three South Australian sites (mean reduction $\approx$18.7\%). Overall, RGMR provides a practical route for deploying frozen TSFMs in regional climate forecasting workflows.
Comments23 pages, ICML2026 Accepted Paper(Poster)
