The Indian Summer Monsoon Rainfall (ISMR) contributes to 80% of India’s annual precipitation, affecting 1.4 billion people. The performance of CMIP6 models in simulating ISMR is still inadequate. Traditional dynamic or statistical downscaling techniques have only modestly improved ISMR simulations. While deep learning models like YNet have shown promise in global downscaling efforts, we found their application to ISMR has been less effective, particularly for extreme rainfall and intraseasonal variability. We developed two advanced super-resolution deep learning models to overcome these limitations: YNet_D and YNet_DE. YNet_D enhances the downscaling accuracy by integrating atmospheric variables, informed by monsoon dynamics, with coarse precipitation data as predictors. YNet_DE prioritizes extreme rainfall using a weighted loss function, effectively addressing the challenge of simulating extremes. Both models outperform YNet, with YNet_D and YNet_DE achieving root mean squared errors (RMSE) of 12.25 mm and 11.97 mm, respectively, compared to YNet’s 12.41 mm. In terms of extremes, YNet_D and YNet_DE reduced the bias in the 95th percentile (R95p) from 78.27 mm with YNet to 56.52 mm and 40.22 mm, respectively. Additionally, both models showed significant improvements in simulating ISMR’s intraseasonal and interannual variations and demonstrated strong transferability to General Circulation Models (GCMs), with YNet_DE notably lowering the multi-model mean bias for R95p from 114.3 mm in the original GCMs and 130.0 mm in YNet simulations to 64.4 mm. Unlike conventional statistical methods, these models retain the critical physical dynamics of ISMR, offering a robust solution that preserves the system’s variability and complexity.

Vegetation productivity in India varies at intraseasonal to interannual time scales, influenced by meteorological factors sensitive to large-scale climate teleconnections. While the impact of global climate variability on Indian monsoon and its extremes is well known, their effects on Indian vegetation productivity are relatively less understood. This study addresses this gap by decomposing dominant modes of spatio-temporal variability of gross primary productivity (GPP) over India and examining their dependence on climate teleconnections. We found that El-Niño Southern Oscillation (ENSO) and Pacific Meridional Mode (PMM) significantly impact GPP, especially in western and southern peninsular India during the monsoon and post-monsoon seasons. However, there is an east-west asymmetry in the PMM-GPP correlation. The western region and southern peninsula are negatively correlated, while northeast India positively correlates with PMM. Using wavelet decomposition, we show that more than half of temporal variability in the GPP comprises low-frequency components. These low-frequency signals primarily drive the relationship between GPP and climate teleconnections. Next, we identify the dominant spatial modes of low-frequency signals of GPP. We tested the predictability of the principal components of GPP using teleconnections and hydrometeorological variables. While most of the predictive skill of GPP comes from its past (memory up to 5 months, R2 score of up to 0.5), adding teleconnection indices as predictors improves the prediction skill at lead times (with an increase of 0.1-0.2 in R2 values). Our results underscore the utility of using hydrometeorological and distant climate teleconnection in GPP prediction for longer lead times.

A knowledge gap in our understanding of the global carbon and water cycle, which in turn impacts climate science insights and earth systems model development, is the relative dominance of the drivers of vegetation productivity and subsequently the atmospheric carbon-dioxide growth rates (CGR). There is a lively debate in the literature based on correlation or bivariate causal methods, on whether the primary driver(s) is (are) temperature (T) or water availability, specifically, precipitation (P), soil moisture (SM), and/or terrestrial water storage (TWS). Our data-driven causal analyses, which rely on Granger Causality (GC) and the Peter and Clarke Momentary Conditional Independence (PCMCI) tests, consider all potential causal variables holistically, and reveal two primary atmospheric drivers for net ecosystem exchange and CGR: vapor pressure deficit (VPD) and photosynthetically active radiation (PAR). Our findings challenge recent literature, underscore the critical role of evaporative demand and vegetation dynamics in atmospheric carbon sensitivity, and highlight the need for more robust causal testing methods.

10% coefficient of variation (COV) in All India Monsoon Rainfall (AIMR) assumes paramount importance as the government classifies years with AIMR above 110% as surplus and below 90% as deficit, shaping critical decisions. While most Indian regions exhibit a 20-40% COV for grid-level monsoon rainfall, the mystery surrounding AIMR’s 10% COV persists. An examination of 40 years of gridded monsoon rainfall data exposes the negative covariance between Central India (CI) and Northeast India (NE) constraining the COV of AIMR to approximately 10%. Utilizing Lagrangian backtracking of moisture, we found that during CI’s deficit rainfall years, NE experiences surplus with 61.69% moisture from terrestrial sources. In CI’s surplus years, major fraction of the moisture supply from ocean is confined to CI, resulting in NE facing deficit rainfall. Our findings emphasize the non-intuitive processes leading to the 10% COV in AIMR cautioning against relying on this value for scientific studies or planning.