the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Nov 2025
Rainfall regimes, their transitions, and long-term changes during Indian summer monsoon
Abstract. We present a diagnostic framework and accompanying dataset of daily rainfall regimes during the Indian Summer Monsoon (ISM) for June–September 1961–2018. Using high-resolution (0.25°) daily rainfall and unsupervised k-means clustering, eleven objectively defined spatial rainfall patterns were identified and linked with characteristic low-level winds, sea-level pressure and moisture fields, separating different modes of active and break phases. The dataset provides (i) centroid rainfall patterns of each regime and (ii) daily cluster IDs, enabling reconstruction of the full temporal sequence of rainfall regimes and calculation of transition probabilities between states. Transition analysis confirms that break phases are the most persistent while monsoon depressions are more transient, mirroring observed synoptic life cycles. A decomposition of rainfall change between 1961–1989 and 1990–2018 shows that drying in Northeast India (∼9 %) is driven by fewer break periods with northeast-focused rainfall, whereas Gangetic Plain drying (∼7 %) is linked to both intensity and frequency changes. This regime-based approach provides a powerful diagnostic tool to examine synoptic drivers, long-term changes in rainfall intensity and frequency, regime transition dynamics, and to evaluate model representation of ISM variability, teleconnections and trend attribution.
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Status: open (until 05 Jan 2026)
- CC1: 'Comment on egusphere-2025-4585', Nima Zafarmomen, 03 Dec 2025 reply
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RC1: 'Comment on egusphere-2025-4585', Adway Mitra, 09 Dec 2025
reply
The paper focuses on identifying spatial patterns of Indian Monsoon rainfall using K-means clustering. These patterns are identified with specific phases of Indian Monsoon, such as the break phase with low overall rainfall, and several other phases, each of which is characterized by rainfall over some particular region like the Indo-Gangetic Plane, Northeastern India or Kutch and Thar. It is argued that these 10-odd clusters represent the spatial distribution of rainfall on most of the days in the study period, it is assumed that these patterns follow Markovian dynamics, and transition probabilities are worked out with some physical justifications. The change in the frequency of each of the clusters/patterns and the intensity of rainfall associated with them is studied across the entire study period, and such change is considered as a marker of climate change. I have the following observations about this paper:
1) Originality - This is not the first work that aims to identify a set of "canonical" patterns of rainfall. MISO or Monsoon Intra-Seasonal Oscillations (Suhas et al, 2013, "An Indian monsoon intra-seasonal oscillations (MISO) index for real time monitoring and forecast verification") and the binary Random Fields (Mitra et al, 2019, "Spatio-temporal patterns of daily Indian summer monsoon rainfall ", Sharma et al, 2020, "Spatio-temporal relationships between rainfall and convective clouds during Indian monsoon through a discrete lens" ). The patterns identified here have significant similarity with these patterns, and hence some sort of qualitative and quantitative comparison is needed, along with a justification of why at all more patterns like this is needed.
2) Choice of K-means - It is not very clear what was considered as the feature vectors for clustering. Is it vectorized form of the rainfall values of each day? If so, we may be missing out on the spatial aspects due to vectorization (2D to 1D). Also, what is the nature of the space on which we are doing the clustering? What kinds of clusters are desired, and is K-means the ideal way to do it? What is the within-cluster variance and across-cluster separations?
3) What is the basis of considering the first-order Markovian dynamics of the clusters? What is the physical justification of it? How many days does each cluster persist and why? The kind of transition dynamics that has been estimated - how can it be justified using known processes (oscillations etc) associated with the Monsoon>
The paper may become suitable for acceptable and publication after these queries are answered and the paper is modified accordingly.
Citation: https://doi.org/10.5194/egusphere-2025-4585-RC1
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The manuscript presents a regime-based analysis of Indian Summer Monsoon (ISM) rainfall for JJAS 1961–2018, using:
IMD 0.25° daily rainfall
k-means clustering to identify 11 spatial rainfall regimes
NCEP–NCAR Reanalysis-1 to characterize circulation, pressure, and moisture anomalies for each cluster
Markov-chain style transition probabilities between regimes
A decomposition of rainfall change (1961–1989 vs 1990–2018) into frequency vs intensity contributions by regime and region.
The study aims to link objectively derived rainfall regimes to synoptic drivers (depressions, trough position, breaks, etc.), examine their transition dynamics, and use these regimes to interpret long-term regional rainfall changes (particularly over Thar & Kutch, Indo-Gangetic Plains, and Northeast India).