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arxiv: 2605.01326 · v1 · submitted 2026-05-02 · ⚛️ physics.ao-ph

Recognition: unknown

Prediction and Predictability of the Wet-Season Rainfall over Southeast India

Harini S , Devabrat Sharma , Yogenraj Patil , Gaurav Chopra , Shruti Tandon , B. N. Goswami , R. I. Sujith
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:20 UTC · model grok-4.3

classification ⚛️ physics.ao-ph
keywords Tamil Nadu rainfallwet season predictiontropical SST networkIndian monsoonseasonal predictabilitysea surface temperatureclimate variabilitylong-lead forecast
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The pith

Global tropical sea surface temperature patterns enable rainfall forecasts for southeast India up to ten months ahead.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates the seasonal predictability of wet-season rainfall over Tamil Nadu in southeast India amid rising variability from climate change. It documents increases in rainfall amounts, variability, and rainy season length, attributing excess rain to reduced convective inhibition from higher temperatures and moisture. The central finding is that a global network of tropical sea surface temperatures supports high potential predictability, with significant forecast skill possible at lead times up to 10 months. This long-lead skill arises from interactions across the Indo-Pacific and equatorial Atlantic regions, while zero-lead skill is dominated by North Indian Ocean temperatures. The work outlines a data-driven approach to making useful forecasts despite the challenges of non-stationary conditions.

Core claim

A global tropical SST climate network reveals high potential predictability for the October-December rainfall over Tamil Nadu, with significant forecast skill possible at lead times up to 10 months. This long-lead predictability stems from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. At zero lead time, predictability is dominated by North Indian Ocean SST anomalies. The study also identifies an overall increase in monthly rainfall and its variability linked to higher surface temperatures, water vapour, and moisture convergence, attributed to a long-term reduction in convective inhibition, plus an increasing trend in rainy season length due<f

What carries the argument

The global tropical SST climate network, which uses sea surface temperature anomalies and their interactions with rainfall across the tropical Indo-Pacific and equatorial Atlantic to generate long-lead predictions of Tamil Nadu wet-season rainfall.

If this is right

  • Skillful seasonal forecasts for Tamil Nadu rainfall can be achieved using global tropical SST patterns at lead times up to 10 months.
  • The data-driven methodology supports useful predictions even with the documented rise in rainfall variability.
  • Local North Indian Ocean SST anomalies provide the main control on simultaneous (zero-lead) rainfall predictability.
  • The length of the rainy season over southeast India is increasing because of earlier monsoon onset and later withdrawal.
  • Excess rainfall trends result from long-term reduction in convective inhibition driven by warming and higher moisture.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Earlier forecasts could improve agricultural planning and water management decisions in Tamil Nadu by several months.
  • The same network approach may apply to other sub-regions of the Indian monsoon that face similar increases in variability.
  • Periodic retraining of the network on newer data would likely be needed to preserve skill as non-stationarity continues.
  • Direct comparison of the network's predictions against observed rainfall in the most recent independent years would test persistence of the claimed skill.

Load-bearing premise

The observed statistical relationships between global tropical SST patterns and Tamil Nadu rainfall will persist and deliver real forecast skill even as rainfall variability increases and climate conditions become more non-stationary.

What would settle it

Applying the trained global tropical SST network to rainfall observations from years after the training period and checking whether skill at 10-month lead times remains statistically significant above simpler benchmarks or random chance.

read the original abstract

The challenge in predicting sub-regional climate within the Indian monsoon region is exacerbated by its increasing variability in a warming world. While exploring the seasonal predictability of rainfall over the state of Tamil Nadu in southeast India, we identify an overall increase in the monthly rainfall and its variability in recent years due to an increase in surface temperature, water vapour and moisture convergence. We attribute the increasing excess rainfall to a long-term reduction in convective inhibition. We further find an increasing trend in the length of the rainy season due to an earlier onset and a delayed withdrawal of the large-scale monsoon over the southeastern and southwestern regions of southern peninsular India, respectively. Further, the simultaneous (0- month lead) predictability of the primary wet-season (October-December, OND) rainfall over Tamil Nadu is dominated by sea surface temperature (SST) anomalies in the North Indian Ocean. However, a global tropical SST climate network reveals a high potential predictability and potential to realize significant forecast skill at a lead time of up to 10 months. The long-lead predictability arises from SST and rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic regions. Our findings provide a robust data-driven methodology for skillful seasonal rainfall prediction over Tamil Nadu, despite the increasing rainfall variability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript reports increasing monthly rainfall and variability over Tamil Nadu in recent decades, attributed to rising surface temperatures, water vapor, moisture convergence, and a long-term reduction in convective inhibition. It also documents an extended rainy season via earlier onset and delayed withdrawal. For predictability, simultaneous (0-month lead) OND rainfall is dominated by North Indian Ocean SST anomalies, while a global tropical SST climate network is said to reveal high potential predictability and realizable forecast skill at leads up to 10 months, arising from SST-rainfall interactions across the tropical Indo-Pacific and equatorial Atlantic. The work proposes a data-driven methodology for skillful seasonal prediction despite the noted variability.

Significance. If the central predictability claims hold after proper validation, the paper would offer a useful data-driven framework for long-lead regional rainfall forecasting in southeast India, an agriculturally critical area facing increasing variability. The mechanistic links to reduced convective inhibition and identification of key Indo-Pacific/Atlantic teleconnections provide physical insight into monsoon changes under warming. The emphasis on a global climate network approach is a constructive contribution that could be extended if methods are made transparent and reproducible.

major comments (2)
  1. [Abstract] Abstract: the headline claim of 'high potential predictability' and 'significant forecast skill' at up to 10-month leads from the global tropical SST climate network is load-bearing for the paper's contribution, yet the manuscript supplies no details on data periods analyzed, network construction (node selection, correlation thresholds, or community detection), statistical model employed, or validation (cross-validation, independent test periods, or skill metrics with error bars). Without these, it is impossible to determine whether reported skill exceeds persistence or is inflated by in-sample fitting.
  2. [Results] Results section on trends and predictability: the paper documents non-stationary behavior (increasing rainfall variability, earlier onset, delayed withdrawal) driven by thermodynamic changes, but provides no explicit tests of whether the underlying SST-rainfall relationships remain stable (e.g., split-sample correlations before/after trend onset, rolling-window analysis, or training on pre-1990 data and testing on later periods). This directly affects whether historical associations can be expected to deliver realizable long-lead skill.
minor comments (2)
  1. [Abstract] The abstract refers to 'data-driven methodology' without naming the precise statistical or network-based algorithm (e.g., whether linear regression, random forests, or graph-based prediction is used), which hinders immediate reproducibility.
  2. Figure captions and text should consistently report the exact years of the observational datasets (e.g., SST and rainfall records) and any detrending or filtering applied.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments highlight important issues of methodological transparency and robustness that we address below. We have revised the manuscript to incorporate the requested details and tests.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the headline claim of 'high potential predictability' and 'significant forecast skill' at up to 10-month leads from the global tropical SST climate network is load-bearing for the paper's contribution, yet the manuscript supplies no details on data periods analyzed, network construction (node selection, correlation thresholds, or community detection), statistical model employed, or validation (cross-validation, independent test periods, or skill metrics with error bars). Without these, it is impossible to determine whether reported skill exceeds persistence or is inflated by in-sample fitting.

    Authors: We agree that the abstract and main text require greater explicitness on these elements to support the predictability claims and enable reproducibility. In the revised manuscript we expand the abstract to reference the key methodological choices and add a dedicated Methods subsection that specifies the data periods, network construction procedure (including node selection, correlation thresholds, and community detection), the statistical model, and the validation approach with skill metrics and uncertainty estimates. We also include direct comparisons to persistence forecasts. revision: yes

  2. Referee: [Results] Results section on trends and predictability: the paper documents non-stationary behavior (increasing rainfall variability, earlier onset, delayed withdrawal) driven by thermodynamic changes, but provides no explicit tests of whether the underlying SST-rainfall relationships remain stable (e.g., split-sample correlations before/after trend onset, rolling-window analysis, or training on pre-1990 data and testing on later periods). This directly affects whether historical associations can be expected to deliver realizable long-lead skill.

    Authors: The referee correctly notes that non-stationarity could affect the reliability of long-lead predictions. Although the manuscript links trends to thermodynamic drivers, it does not include formal stability diagnostics. In the revision we add split-sample correlation analyses (pre- and post-1990), rolling-window correlations, and out-of-sample tests (training on earlier data and evaluating on later periods) to assess whether the key SST-rainfall teleconnections remain stable. These results will be presented in the Results section with accompanying figures. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on data-driven network analysis without self-referential reduction shown

full rationale

The abstract presents the global tropical SST climate network as revealing long-lead predictability for Tamil Nadu OND rainfall based on observed SST-rainfall interactions across Indo-Pacific and Atlantic regions. No equations, fitting procedures, or self-citations are quoted that would reduce the claimed predictability or forecast skill to quantities fitted directly from the target data by construction. The paper separately documents non-stationary trends in rainfall and attributes them to physical changes, but these are presented as independent observations rather than load-bearing for the predictability claim. Without explicit reduction of the network-derived skill to the same historical inputs (e.g., via in-sample fitting without validation), the derivation chain remains self-contained and does not match any enumerated circularity pattern.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The abstract relies on standard domain assumptions about SST-rainfall teleconnections without introducing explicit free parameters, new entities, or ad-hoc axioms beyond typical climate science background.

axioms (1)
  • domain assumption Sea surface temperature anomalies influence rainfall variability in the Indian monsoon region through moisture and circulation changes
    Invoked implicitly in both the trend attribution and the predictability sections of the abstract.

pith-pipeline@v0.9.0 · 5542 in / 1418 out tokens · 87859 ms · 2026-05-10T16:20:56.740006+00:00 · methodology

discussion (0)

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