Skillful forecasts of hydroclimate variables are essential for operational water management, agricultural planning, and food supply. Several studies have attempted to improve the skill of raw forecasts either by post-processing or by incorporating sea surface conditions into raw forecasts. However, to the best of our knowledge, limited to no study has investigated temporal trend, which is present in observed records but is absent from retrospective forecasts (also known as, hindcasts). The current study understands that a temporal trend can be yielded in raw meteorological forecasts by i) updating surface boundary forcings and ii) applying statistical models for either post-processing meteorological forecasts or issuing streamflow forecasting using weather forecasts as predictors. To analytically derive the relationship between temporal trend and forecast performance, this study applies three statistical approaches for post-processing season-ahead hindcasts of the Indian monsoon obtained from three general circulation models (GCM). The findings show that raw hindcasts of the Indian monsoons typically ignore the temporal trend present in the observed records. Furthermore, analytical derivations confirm that the absence of a trend in GCM hindcasts significantly influences post-processing performance. Moreover, a semi-parametric approach could not overcome the limitations of a parametric linear model in yielding a temporal trend in the hindcasts. Potential reasons for the absence of a trend in the hindcast is also discussed.

中文翻译：

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长期观测趋势对季节性水文气候预报性能的影响


熟练地预测水文气候变量对于水资源管理、农业规划和粮食供应至关重要。一些研究试图通过后处理或将海面状况纳入原始预报来提高原始预报的技能。然而，据我们所知，还没有研究调查时间趋势，这种趋势存在于观察记录中，但在回顾性预测（也称为后报）中不存在。目前的研究认为，通过i）更新地表边界强迫和ii）应用统计模型进行后处理气象预报或使用天气预报作为预测因子发布水流预报，可以在原始气象预报中产生时间趋势。为了分析推导时间趋势和预报性能之间的关系，本研究应用三种统计方法对从三个大气环流模型（GCM）获得的印度季风季风后报进行后处理。研究结果表明，印度季风的原始后报通常忽略了观测记录中存在的时间趋势。此外，分析推导证实，GCM 后报趋势的缺失会显着影响后处理性能。此外，半参数方法无法克服参数线性模型在产生后报时间趋势方面的局限性。还讨论了后报中缺乏趋势的潜在原因。