BiLSTM-Kalman framework for precipitation downscaling under multiple climate change scenarios.

Melika Jahangiri, Mahdi Asghari, Mohammad Hossein Niksokhan, Mohammad Reza Nikoo

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Melika JahangiriFaculty of Environment, University of Tehran, Tehran, Iran.

Mahdi AsghariFaculty of Environment, University of Tehran, Tehran, Iran.

Mohammad Hossein NiksokhanFaculty of Environment, University of Tehran, Tehran, Iran. niksokhan@ut.ac.ir.ORCID https://orcid.org/0000-0001-8172-696X

Mohammad Reza NikooDepartment of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.ORCID https://orcid.org/0000-0002-3740-4389

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional downscaling techniques often fail to accurately represent critical extremes necessary for effective adaptation planning. This paper introduces the first application of Bidirectional Long Short-Term Memory (BiLSTM) networks with an adaptive Kalman filter for multi-scenario, high-resolution precipitation downscaling. We applied our methodology to Tehran, Iran, and systematically compared and ranked the performance of different CMIP6 projections, with the best performing model being MIROC (NSE: 0.902, R

Indexed as

Adaptive Kalman filteringBiLSTM neural networksClimate change scenariosDeep learningExtreme event forecastingPrecipitation downscaling

Identifiers

PMID40628842

DOI10.1038/s41598-025-08264-z

PMCPMC12238583

What OpenQuestion holds

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.