We revisit long-standing controversies regarding relationships among the all-India rainfall index (AIRI), sub-India summer rainfall variations, El Niño-Southern Oscillation (ENSO), and the Indian Ocean Dipole (IOD) using 120-year sea surface temperature and high-resolution rainfall datasets. AIRI closely tracks with the spatial extent of wet anomalies and with the average across gridpoints in rainy day count. The leading rainfall variability mode is a monopole associated primarily with rainy day count and ENSO. The second mode is a tripole with same-signed loadings in the high-rainfall Western Ghats and Central Monsoon Zone regions and opposite-signed loadings in Southeastern India between. The IOD projects onto this tripole and, as such, is weakly correlated with AIRI. However, when the linear influence of ENSO is removed, the IOD rainfall regressions become quasi-homogeneously more positive, making the ENSO-residual IOD and AIRI timeseries significantly correlated.
Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes
Gauhati University（古哈提大学）；Indian Institute of Technology Madras（马德拉斯印度理工学院）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；Cotton University（科顿大学）
Multiscale interactions between monsoon intra-seasonal oscillations and low pressure systems that produce heavy rainfall events of different spatial extents
Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon
Indian Institute of Technology (IIT) Madras（印度理工学院马德拉斯分校）；CSIR National Institute of Data Science and Artificial Intelligence（印度科学、技术和医学研究理事会数据科学与人工智能国家研究所）；IIT Delhi（印度理工学院德里分校）
Deep-learning based down-scaling of summer monsoon rainfall data over Indian region
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所，地球科学部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Ramrao Adik Institute of Technology（拉姆劳·阿迪克技术学院）；Defence Institute of Advanced Technology（国防高级技术研究所）
