The theoretical and practical understanding of projected changes in rainfall is desirable for planning and adapting to climate change. In this study, finite size Lyapunov exponents (FSLE) are used to study error growth rates of the system at different timescales. This is done to quantify the impact of enhanced anthropogenic greenhouse gas emissions on the predictability of fast and slow varying components of central Indian rainfall (CIR). The CIR time series for this purpose is constructed using the daily gridded high-resolution India Meteorological Department (IMD) dataset and Coupled Model Inter-comparison Project phase 5 (CMIP5) output for historical run and three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) from the HadGEM2-ES, IPSL-CM5A-LR, CCSM4, BCC-CSM1.1, and MPI-ESM-LR models. The analyzed CIR dataset reveals a low dimensional chaotic attractor, suggesting that CIR requires a minimum of 5 and maximum of 11 variables to describe the state of the system. FSLE analysis shows a rapid decrease in the Lyapunov exponent with increasing timescales. This analysis suggests a predictability of about 2–3 weeks for fast varying components at short timescale of the CIR and about 5–9 years for slow varying components at long timescales.

对于计划和适应气候变化，需要对降雨预计变化的理论和实践理解。在这项研究中，有限大小的Lyapunov指数（FSLE）用于研究系统在不同时间范围内的误差增长率。这样做是为了量化人为温室气体排放量增加对印度中部降雨（CIR）的快慢变化分量的可预测性的影响。为此，使用每日网格化的高分辨率印度气象部门（IMD）数据集和历史运行的耦合模型比较项目第5阶段（CMIP5）输出和三个代表性浓度路径（RCP2.6，RCP4）构建了CIR时间序列.5和RCP8.5）来自HadGEM2-ES，IPSL-CM5A-LR，CCSM4，BCC-CSM1.1和MPI-ESM-LR模型。分析的CIR数据集揭示了一个低维混沌吸引子，表明CIR需要最少5个变量和最多11个变量来描述系统状态。FSLE分析显示，随着时间尺度的增加，李雅普诺夫指数迅速下降。该分析表明，在CIR的短时间范围内，快速变化的组件的可预测性约为2-3周，而在长时间范围内，缓慢变化的组件的可预测性约为5-9年。