Ireland's water-resources planning is structured around Regional Water Resources Plans (RWRPs), which subdivide Regional Water Management Areas into Study Areas (SAs) comprising one or more Water Resource Zones (WRZs). The GDA broadly aligns with Uisce Éireann's (Ireland's national water utility) Study Areas SA1, SA2 and SA9. SA1 and SA2 encompass the eastern and western parts of the northern Wicklow Mountains, including steep mountain slopes, upland valleys and headwater catchments. The upland domains are strongly exposed to moisture-bearing Atlantic air masses, particularly under prevailing south-westerly and westerly circulation, enhancing orographic precipitation relative to lowland SA9. Elevation ranges from lowland foothills and valley floors to more than 800 m in the Wicklow Mountains, producing pronounced orographic rainfall gradients. SA9 is predominantly urban and spans County Dublin and parts of adjacent counties, with a population of approximately 1.7 million. The GDA water supply system is substantially reliant on the River Liffey, increasing sensitivity to prolonged rainfall deficits and operational shocks (Uisce Éireann, 2022a, b). For precipitation reconstruction, SA9 is retained as defined by Uisce Éireann, while SA1 and SA2 are combined into a single upland domain (hereafter SA-upland) to reflect hydrological similarity (Fig. 1). Reconstructions are produced and evaluated separately for SA9 and SA-upland and evaluated against gridded observational rainfall.

Observed monthly precipitation is represented using a 1 km gridded rainfall dataset (1865–2024) produced by Met Éireann. Inputs are drawn from the modern station network after 1941, while earlier decades incorporate extensive rescued historical gauge data, including Rainfall Rescue digitisation (Hawkins et al., 2023) and additional Met Éireann transcriptions (Ryan et al., 2021; Ryan and McGovern, 2025). Grids are generated using regression on physiographic predictors and kriged residuals, and evaluated by cross-validation (Coonan et al., 2024). A single series of monthly totals for each domain was extracted from the gridded data for use in subsequent analysis, with the contributing stations and 1 km grid cells shown in Fig. S1 in the Supplement. As with all long-term gridded gauge-based precipitation products, station density and record continuity are generally lower in complex terrain and in earlier periods (Hofstra et al., 2009; Newman et al., 2015; Cornes et al., 2018). Interpolation can smooth small-scale variability, dampen local extremes relative to point observations, and may introduce larger uncertainties in upland areas where rainfall gradients are strong (Daly, 2006; Hofstra et al., 2009; Herrera et al., 2012; Daly et al., 2021; Matiu et al., 2024). Gridded totals also inherit known gauge-measurement biases – particularly exposure, wind and snow-induced undercatch (Adam and Lettenmaier, 2003; Murphy et al., 2018; Pollock et al., 2018; Hawkins et al., 2023; Kendon et al., 2023).

The Jenkinson dataset is a documentary-instrumental rainfall anomaly series for the island of Ireland derived from an unpublished UK Meteorological Office note compiled by Jenkinson et al. (1979). It provides continuous Ireland-wide monthly rainfall estimates for 1711–1977, expressed as (i) annual rainfall anomalies (annual totals expressed as a percentage of an estimated annual average rainfall, AAR) and (ii) proportional monthly rainfall distributions expressed as “internal percentages” of the annual total. Resampling AAR for the GDA allows the annual anomaly and monthly distribution information to be re-anchored to the rainfall climatology for the Dublin domains considered here.

The Jenkinson series draws on a changing combination of source materials through time including early rain-gauge observations, documentary weather diaries, regional UK rainfall series used as proxy stations, and later Irish station observations. The full Jenkinson series, its compilation and provenance are described in detail by Murphy et al. (2018). For much of the eighteenth century, the series is strongly informed by Rutty's Dublin weather diary (Rutty, 1770). From 1792 to 1839, observed Dublin rainfall measurements reported by Dixon (1953), supplemented by manuscript records held by the UK Met Office, contribute to the reconstruction, while additional monthly and annual totals from Dublin are included for 1823–1824. From 1840 to 1977, the reconstruction increasingly incorporates instrumental observations from a growing Irish station network, including Dublin, Belfast, Armagh, Cork and Sligo. During 1940–1948, the series further draws on monthly rainfall totals from ten long-term Irish stations (Tabony, 1980) together with annual rainfall maps produced by the UK Meteorological Office (1940–1945) and the Irish Meteorological Service (1946–1948). From 1949 to 1977, monthly and annual rainfall observations from the Irish Meteorological Service/Met Éireann provide the principal observational input to the reconstruction. The Jenkinson series is not fully independent of the gridded observational rainfall used for evaluation, particularly during periods when both products draw directly or indirectly on Dublin-area station records. Nevertheless, the series provides a valuable long-term documentary-instrumental estimate of monthly rainfall variability.

Plausible predictors with potential utility for reconstructing monthly precipitation were compiled to develop statistical reconstructions. The predictor set comprises quality-assured sea-level pressure (SLP) datasets, North Atlantic/European circulation indices, and auxiliary hydroclimate covariates, including Central England Temperature (CET), a monthly UK precipitation series, and the summer Palmer Drought Severity Index (PDSI) derived from the Old World Drought Atlas (Cook et al., 2015). All predictors used, their sources, and temporal coverage are summarised in Table 1. Reconstructions were undertaken from 1748 to 1994 given the overlap of available predictors.

This study uses tree-ring oxygen isotope (δ18O) data from the master chronology compiled by Loader et al. (2019) for Central England, spanning 1200–2000 CE, based on δ18O measurements of oak tree-ring cellulose, to derive an annually resolved reconstruction of MJJA (May–August) precipitation variability. We use this dataset to provide an annually resolved proxy archive of growing-season hydroclimate variability from the wider British-Irish Isles region. Here, it is used to derive a MJJA precipitation reconstruction for the Dublin rainfall domains by calibrating against observed MJJA rainfall during the instrumental overlap period. The purpose of including the δ18O-based reconstruction is therefore twofold: first, to provide an independent proxy-based comparison for summer hydroclimate variability during the period of overlap with gridded observations and the documentary-instrumental reconstructions; and second, to extend the assessment of regional summer hydroclimatic variability beyond the documentary-instrumental window. Its interpretation is restricted to MJJA rainfall variability and the longer-term summer hydroclimate context.

Eastern Ireland and much of the UK lie within a common North Atlantic circulation domain, where precipitation variability at monthly to seasonal scales is modulated by large-scale modes such as the North Atlantic Oscillation and associated moisture-transport regimes (Wilby et al., 1997; Parry et al., 2023). Long-term UK precipitation series were used as regional comparators for assessing the timing and persistence of reconstructed wet and dry periods, particularly before the start of the Irish instrumental record. Four long-term UK monthly precipitation series were employed, including records from Kew Gardens (southern England), the Spalding/Pode Hole composite series (East Midlands), alongside two additional long-duration series from Carlisle (northwest England) and Oxford (south-central England). These series extend into the eighteenth century and are derived from combinations of early instrumental observations and documentary-based reconstructions, with varying degrees of uncertainty in their earliest segments (Wales-Smith, 1971; Craddock and Wales-Smith, 1977; Tabony, 1980; Todd et al., 2013; Murphy et al., 2018). In addition, we compare seasonal totals from our reconstructions with those of Pauling et al. (2006). This comparison is limited to SA9 for the period 1700–2000, as the grid resolution of the Pauling et al. (2006) dataset does not allow for the adequate representation of SA-upland. Seasonal totals from Pauling et al. were extracted for two grids overlapping SA9 and a regional average derived across both cells. Together, these series provide independent regional benchmarks against which the timing, persistence and relative magnitude of reconstructed wet and dry periods can be assessed.

To assess whether early winter rainfall measurements may be biased low because of undercatch of snow or mixed precipitation (Murphy et al., 2019), we use the Dublin snow and sleet dataset (DSS) developed for the GDA (Horvath et al., 2025). DSS summarises the number of days with snow and/or sleet at monthly, seasonal (October–May) and annual scales for 1867–2024, based on six long-running observing sites across the GDA, and is published as an aggregated regional series. Here, DSS is used as an independent indicator of precipitation phase. For analyses requiring longer temporal coverage, DSS is extended back to 1748 using the London snow and sleet series compiled by Gordon Manley (1969), which was subsequently digitised and extended to 1974 by Murphy et al. (2019). The Manley series is based on a synthesis of early instrumental observations and documentary evidence from the greater London area and provides one of the longest continuous records of snowfall and sleet occurrence in the North Atlantic region.

Statistical reconstructions were developed separately for each rainfall domain and calendar month. Two regression approaches were applied: Lasso and Random Forest (RF) regression. Models were fitted using the 1930–1994 period, given the denser modern gauge network within the gridded observational dataset. Four-fold blocked cross-validation was used within the 1930–1994 period. Calibration years were ordered chronologically and divided into four contiguous blocks with out-of-fold predictions generated from temporally withheld data. These out-of-fold predictions were used to tune model parameters, derive ensemble weights and to summarise model development performance. Selected LASSO and RF models were then refitted using the full 1930–1994 period and applied to the full record to generate monthly reconstructions for 1748–1994. Monthly predictions were subsequently aggregated to annual and seasonal totals. Seasons were defined as DJF, MAM, JJA, SON and MJJA, with December assigned to the following winter season-year for DJF aggregation. The 1930–1994 out-of-fold results are interpreted as blocked cross-validation model development diagnostics rather than as fully independent validation, because the same model development period was used for hyperparameter selection and ensemble weight derivation. Independent temporal validation was undertaken separately over 1865–1929, which was not used for model fitting, hyperparameter tuning or ensemble weighting. Model performance was summarised using root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation coefficient, Spearman's rank correlation coefficient and percent bias (PBIAS).

Monthly rainfall totals for each study domain were reconstructed from the Jenkinson anomaly-distribution dataset (Jenkinson et al., 1979) for 1711–1977 by anchoring its annual anomalies to a domain-specific mean annual rainfall (AAR) estimated over 1865–1975. Uncertainty in AAR was quantified using a non-parametric bootstrap of the annual totals for each domain from 1865 to 1975. A total of 1000 resamples were generated by drawing years with replacement and calculating AAR for each resample. For each bootstrap AAR, Jenkinson annual percentage anomalies were converted to annual rainfall totals by scaling the AAR upward (for positive anomalies) or downward (for negative anomalies), thereby transferring the interannual variability encoded in Jenkinson onto the domain-specific climatological mean annual rainfall (AAR). Annual totals were then partitioned into monthly totals using the corresponding year's internal percentages (converted to proportions). Final monthly estimates were summarised across the 1000-member ensemble using the median and the 2.5th and 97.5th percentiles as uncertainty intervals. These intervals reflect uncertainty in the anchored AAR estimate and are conditional on the Jenkinson anomalies and internal-percentage fields.

Extended summer rainfall (MJJA) for each GDA domain was reconstructed from an annually resolved oak cellulose δ18O master chronology for Central England using a reduced major axis variance-scaling approach as applied by Loader et al. (2020). For each domain, the calibration relationship was defined from the overlap period between δ18O and observed MJJA rainfall over the period 1930–2000. Calibration-period means and standard deviations of δ18O and MJJA rainfall, together with their correlation, were used to derive the transformation, which was then applied to all years with available δ18O to generate a reconstructed MJJA rainfall series.

Uncertainty in reconstructed MJJA rainfall was quantified using 1000 bootstrap resamples of annual calibration-year pairs (δ18O, MJJA rainfall) drawn with replacement (Efron and Tibshirani, 1993). For each replicate, calibration summary statistics and the δ18O-rainfall correlation were re-estimated, and a complete reconstruction was generated using the ensemble median and the 2.5th and 97.5th percentiles as 95 % uncertainty intervals. These intervals quantify uncertainty arising from calibration of the variance-scaling transformation and are conditional on the chosen target series and calibration window.

To assess whether differences between the Jenkinson and statistical ensemble reconstructions may reflect snow- and sleet-related gauge undercatch in early observations, a residual-based diagnostic framework was adopted, following Murphy et al. (2019). The Dublin Snow and Sleet (DSS) series was first extended back to 1748 using the long-term London snow and sleet series (1668–1974) compiled by Gordon Manley (1969) and digitised by Murphy et al. (2019). A linear no-intercept transfer function between DSS and the Manley snow and sleet series was calibrated over their common period (1867–1974) using aggregated annual totals. The fitted relationship was then applied to the Manley series to estimate DSS values for 1748–1866.

Residual analysis was subsequently performed using the difference series between the Jenkinson and ensemble rainfall reconstructions with annual Pearson's r, 10-year Pearson's r and significance of 10-year p-value following adjustment for autocorrelation calculated. This approach enables assessment of whether systematic differences between reconstructions covary with an independent indicator of snowfall and sleet variability, thereby providing an indication of potential snow-related undercatch in early rainfall observations used in the Jenkinson reconstruction.

Decadal analysis was undertaken to account for noise in the annual reconstructions. Comparisons were undertaken for DJF, MAM and the October-May snow year, representing the periods most likely to be affected by solid precipitation. To facilitate comparison, the reconstruction residuals and extended snow and sleet series were converted to z-scores using their respective means and standard deviations.

Comparison of performance across the different reconstructions is undertaken using visual assessment and quantitative evaluation for the full period of overlap with observations. Key evaluation metrics include Pearson's r to assess covariation, R2 to examine the proportion of variance in the observations captured by each reconstruction and the Percent Bias (PBIAS) and Mean Absolute Error (MAE). The ability of each reconstruction to capture wet and dry extremes in the observations is evaluated using the Probability of Detection (POD) and False Alarm Ratio (FAR) (Murphy et al., 2020; Rudd et al., 2017). Based on threshold exceedances, the former measures the proportion of observed extremes successfully detected by the reconstructions, while the latter measures the proportion of extremes in the reconstructions that were not observed. The metrics are calculated as follows: 1POD=HH+M2FAR=FH+F where hits (H) count the number of events which occurred in both the observations and the reconstruction, misses (M) indicate the number of events that occurred in the observed series but not in the reconstruction. The False Alarm (F) counts instances where an event occurred in the reconstruction but not in the observed series.

For continuous monthly reconstructions (Jenkinson and Statistical Ensemble), the Standardised Precipitation Index (SPI) was derived for both reconstructions and observations. SPI was fitted for accumulation periods of 1, 3, 6 and 12 months using a Gamma distribution. POD and FAR were evaluated at thresholds of ±1.0 and ±1.5, representing moderate and extreme droughts and pluvials. For MJJA precipitation, per cent anomalies relative to a fixed (1980–2000) baseline, were calculated for each series. Positive and negative thresholds of ±20 % and ±50 % of the baseline for each month were identified.

We separately assess the consistency of the 20 wettest and 20 driest years between 1865 and 1977 for each reconstruction relative to observations using the Jaccard Similarity Coefficient (Jaccard Index, JI). For our subset of the top 20 wet and dry years, the JI is used to measure the similarity/diversity of sample sets and is calculated as: 3JIA,B=|A∩B||A∪B| Where the numerator represents the intersection of the two sets or the number of events in common, and the denominator represents the total number of unique events present across both lists. A JI score of 1 indicates identical sets, 0 indicates complete disjoint (no overlap) while a value of ∼ 0.33 indicates 50 % overlap.

Wettest and driest years were evaluated separately as binary sets. For the wettest-year analysis, the two sets comprised the 20 wettest observed and reconstructed years, respectively; the same procedure was applied independently to the 20 driest years. Years outside the relevant top-20 lists were not assigned to a separate neutral category. For two binary sets, the Jaccard Index is related to the F1 score by JI = F1/(2-F1).

In addition to quantitative evaluation against observations, reconstructed rainfall variability was assessed qualitatively using long-term UK precipitation series (Sect. 2.6). Seasonal totals (DJF, MAM, JJA, SON) were standardised (z-scores) separately for each station and season using the mean and standard deviation over 1711–1977 and then smoothed using 10 year forward running means. These series provide an independent regional benchmark for examining the timing, persistence, and relative magnitude of wet and dry periods in the Dublin pre-instrumental period. Comparisons are based on visual assessment of temporal alignment and consistency across series.

Neither monthly rainfall reconstruction extends to the present. The Jenkinson reconstruction ends in 1977, whereas the statistical ensemble ends in 1994. Gridded observational rainfall is available from 1865 onwards. Therefore, the reconstructed values before 1865 were combined with the common observed rainfall series for 1865–2024. The resulting appended products span 1711–2024 for Jenkinson and 1748–2024 for the statistical ensemble in both study domains. Trends in annual and seasonal rainfall series and their persistence were examined using the Mann-Kendall (MK) test (Mann, 1945; Kendall, 1975). For each series we first calculate the MK Z statistic for the full period of record and then for iteratively shorter record lengths by incrementally dropping the start year, down to a minimum record length of 30 years. This approach assesses the sensitivity of trends to the period analysed (Murphy et al., 2013). Positive and negative Z values indicate increasing and decreasing rainfall trends, respectively, while |Z|≥1.96 denotes statistical significance at the 5 % level.

Given that each method required different calibration approaches, we next compare the performance of each reconstruction across the common overlap with observations 1865–1977. Both the weighted ensemble and Jenkinson reconstructions reproduce observed rainfall variability in SA9 and SA-upland well at annual, seasonal and monthly scales. However, as expected, the Jenkinson reconstruction generally shows the stronger agreement with observations (Table 5). At the annual scale (Table 5 and Fig. 6), both reconstructions capture broad interannual rainfall variability in each domain. The Jenkinson reconstruction returns higher correlations and lower errors than the ensemble. In SA9, annual rainfall correlation is 0.87 for the Jenkinson reconstruction compared with 0.76 for the weighted ensemble, while RMSE falls from 78.62 mm for the ensemble to 60.05 mm for Jenkinson. In SA-upland annual correlation rises from 0.75 for the weighted ensemble to 0.85 for Jenkinson, accompanied by a reduction in RMSE from 130.64 to 102.54 mm. Biases are small for both methods, indicating that the main differences lie in their ability to capture year-to-year variability rather than mean totals.

At the seasonal scale (Table 5 and Fig. 7), the Jenkinson reconstruction again outperforms the weighted ensemble across most seasons and in both domains. In SA9, the highest seasonal performance is obtained for winter and summer. For DJF, correlations are 0.91 for Jenkinson and 0.86 for the ensemble, while in JJA the contrast is larger, with correlations of 0.91 and 0.81, respectively. MAM and SON show the same ordering, with Jenkinson correlations of 0.87 and 0.84, compared with 0.78 and 0.74 for the ensemble. Errors are also consistently lower for Jenkinson, especially in spring, summer and autumn. The same broad pattern is evident in SA-upland. Correlations for the weighted ensemble range from 0.73 in SON to 0.84 in JJA, whereas Jenkinson correlations range from 0.80 in SON to 0.90 in JJA. Thus, although both reconstructions reproduce observed seasonality, the Jenkinson approach provides a closer representation of seasonal rainfall variability in both domains, given the integration of observed data.

The monthly results (Table 5) show a similar, and in some respects even clearer, contrast between the two approaches. For SA9, the weighted ensemble monthly correlations range from 0.74 in July to 0.86 in February, while Jenkinson ranges from 0.82 in October to 0.94 in June. In the SA-upland, ensemble correlations range from 0.75 in April and October to 0.86 in February, while Jenkinson ranges from 0.78 in October to 0.92 in June. Thus, monthly skill is consistently high for both reconstructions, but almost always higher for Jenkinson.

The MJJA season permits comparison among all three reconstruction approaches (Table 6 and Fig. 8). In SA9, the Jenkinson reconstruction shows the strongest agreement with observations, with r=0.90, RMSE = 34.51 mm and MAE = 26.18 mm. The weighted ensemble performs less well, with r = 0.79, RMSE = 41.80 mm and MAE = 32.80 mm. The δ18O reconstruction is markedly weaker, with a correlation of 0.54 and substantially larger errors (RMSE = 67.97 mm; MAE = 54.25 mm). In the SA-upland, the same ranking is observed: Jenkinson again performs best (r=0.89; RMSE = 44.17 mm), followed by the weighted ensemble (r=0.83; RMSE = 52.62 mm), while the δ18O reconstruction remains much weaker (r=0.54; RMSE = 90.91 mm). Notably, all three approaches show relatively small PBIAS values.

The ability of the Jenkinson and weighted ensemble to capture wet and dry extremes is evaluated for the common period of overlap across the reconstructions using SPI-based threshold exceedances (Fig. 9). Across all accumulation periods and thresholds, the Jenkinson reconstruction demonstrates the highest skill at identifying both wet and dry events, with a higher Probability of Detection (POD) and lower False Alarm Ratio (FAR) than the statistical models. This is evident for both moderate (± 1.0) and more extreme (± 1.5) thresholds, where Jenkinson retains comparatively high detection rates, particularly for dry events, which are generally detected more reliably than wet events across all reconstructions. Jenkinson reconstructions also achieve comparatively lower false alarm ratios, with POD values of 0.72 (wet) and 0.76 (dry) for Jenkinson compared to 0.56 and 0.67 for the ensemble for SPI-3, and correspondingly lower FAR values (0.32 and 0.26 vs. 0.43 and 0.39), with similar differences evident across SPI-1, SPI-6 and SPI-12 (Fig. 9).

The statistical reconstructions exhibit lower skill and greater variability. Random Forest performs more favourably than LASSO, particularly for dry events, where POD values approach those of the Jenkinson reconstruction at longer accumulation periods. However, this is often accompanied by elevated FAR (Fig. 9). The weighted ensemble mean typically performs between the two methods, improving on Lasso but not fully matching the performance of Random Forest or Jenkinson. Differences between methods are most apparent at shorter accumulation periods (SPI-1 and SPI-3).

For the more extreme thresholds (± 1.5), detection skill decreases across all reconstructions, reflecting the increased difficulty in identifying rarer events. However, the relative ranking of model performance remains consistent, with Jenkinson retaining the highest POD and generally lower FAR. In contrast, the statistical models show reduced POD and increased false alarm rates, particularly for wet extremes. Dry extremes are generally better captured than wet extremes across all methods (Fig. 9).

The Jaccard Index is employed to evaluate the skill of all three reconstructions in capturing the top 20 wettest and driest years and seasons (Fig. 10), with the full lists of top 20 wettest and driest years provided in Table S4a–b. The Jenkinson reconstruction shows the highest agreement with observations in identifying the top 20 wettest and driest years across most seasons and at the annual scale, with JI values generally exceeding those of the weighted ensemble. For example, in SA9, the Jenkinson reconstruction captures 16 of the 20 wettest (JI = 0.67) and 14 of the 20 driest (JI = 0.54) years, compared to 13 (JI = 0.48) and 10 (JI = 0.33) years, respectively, for the ensemble. This pattern is consistent across seasons in both SA9 and SA-upland (Tables S5–S6), with the Jenkinson reconstruction typically identifying a larger proportion of extreme years than the weighted ensemble. The wettest years (1865–1977) in the observed series for both domains are 1960 and 1872 (ranked third in SA9 and second in SA-upland). Both reconstructions identify these years as exceptionally wet, with 1872 ranked as the wettest, and 1960 ranked second in the Jenkinson reconstruction in both domains, and fifth (SA9) and fourth (SA-upland) in the ensemble (Table S4a–b). The driest years in the observed record are 1887 and 1971. The Jenkinson reconstruction identifies 1887 as the driest year in both domains, while 1971 ranks fourth. The weighted ensemble reconstruction also identifies these years as exceptionally dry, ranking 1887 as the third- and second-driest year in the SA9 and SA-upland domains, respectively, and 1971 as the sixth- and seventh-driest year (Table S4a–b).

In winter (DJF), both reconstructions perform well, capturing 13–15 events (JI = 0.48–0.60), while in spring (MAM) and summer (JJA) the advantage of the Jenkinson reconstruction is more pronounced, identifying 14–15 events (JI = 0.48–0.60) compared to 10–12 events (JI = 0.33–0.43) for the weighted ensemble. In autumn (SON) wet extremes show the weakest agreement, with Jenkinson and the ensemble identifying 11 and 10 of the top 20 events, respectively (JI = 0.38 and 0.33). In contrast, dry extremes are captured more reliably, with 16 and 14 events identified (JI = 0.67 and 0.54, respectively).

Across both domains, dry extremes are generally captured more reliably than wet extremes, with higher Jaccard Index values and a greater number of correctly identified events across most seasons. This is particularly evident in autumn (SON), where dry extremes show the highest agreement (JI up to 0.67), while wet extremes exhibit the lowest agreement (JI typically 0.33–0.38). For MJJA, the δ18O reconstruction identifies 9–11 of the observed top-20 wettest or driest years across the two domains (JI = 0.29–0.38), compared with 12–16 for the weighted ensemble (JI = 0.43–0.67) and 13–16 for Jenkinson (JI = 0.48–0.67). JI values for the δ18O reconstruction are substantially lower than those of both Jenkinson and the ensemble, particularly for dry extremes, indicating reduced skill in identifying individual extreme years. Notably, for SA9, the δ18O reconstruction captures just under 50 % of the observed driest years in MJJA, and just over 50 % of the wettest years. In SA-upland, the δ18O reconstruction identifies nine wettest and ten driest years, while the weighted ensemble shows improved performance in the upland domain, particularly for dry extremes where agreement matches that of the Jenkinson reconstruction.

For winter (DJF), while both Jenkinson and the weighted ensemble show good coherence over the instrumental period, there is evidence of divergence between reconstructions in both domains before circa 1850, with DJF totals for Jenkinson generally lower than for the ensemble (Fig. 7). The divergence is more apparent in the upland domain. A similar pattern is also evident in spring (MAM), but less marked. Given that the Jenkinson series is constructed directly from early observations and documentary sources (Dr John Rutty's weather diary), we explore whether these reconstructions are sensitive to observational bias through the under-representation of snow due to changes in gauge design, observational practice or reliance on documentary sources (Murphy et al., 2020). The weighted ensemble reconstruction is less likely to contain such biases, as it is derived from predictors such as sea level pressure, among others, rather than direct precipitation observations.

To explore whether snowfall and sleet variability may contribute to divergence between reconstructions, a residual series was calculated as the difference between the Jenkinson and weighted ensemble mean and compared with the extended Dublin Snow and Sleet (DSS). A linear transfer function (slope of 0.55 (95 % CI 0.51–0.60) calibrated over the common period (1867–1974) showed good agreement between the Dublin and Manley series (r=0.83; MAE = 4.8 d and Percent Bias of -6.48 %). The derived extended DSS series is shown in Fig. S2. Comparison of 10 year forward-running averages for reconstruction residuals and the inverted DSS series is presented in Table 7 and Fig. 11 for winter (DJF), spring (MAM), and the snow year (ONDJFMAM). For the unsmoothed series, DJF and the snow year show significant positive correlations in both domains after adjustment for autocorrelation, whereas MAM does not (Table 7). For the 10 year smoothed series, only the SA9 snow-year correlation remains significant at the 5 % level after adjustment (p=0.04); the corresponding SA-upland value is 0.08, and DJF values are 0.09 in both domains. Visual correspondence between the residual and DSS series is evident across all seasons and both domains, with periods of elevated snow and sleet occurrence generally coinciding with larger divergence between the reconstructions. Similar behaviour across both domains is consistent with the possibility that the pre-1865 Jenkinson winter and spring series may exhibit low biases due to snowfall undercatch, changing observational practices, or limitations in documentary and early instrumental sources. Comparison with reconstructions and available long-term series from the UK is broadly consistent with the interpretation that winter totals in the 18th-century Jenkinson series may be biased low relative to the weighted ensemble (Fig. 12), while also lending support to the large-scale hydroclimatic variability captured by the weighted ensemble mean. Comparison with Pauling et al. (2006) reconstructions for SA9 winter (DJF) totals provides further support for the possibility that early Jenkinson winter totals are biased low, with the Pauling reconstructions showing stronger agreement with the weighted ensemble reconstruction. The winter Jenkinson reconstruction for SA9 reveals a correlation of 0.68 with the Pauling reconstruction, while the equivalent correlation for the weighted ensemble is 0.73 (Fig. 13).

For Spring (MAM) good coherence is evident between reconstructions and longer-term reconstructions with the Jenkinson series showing lower decadal totals than the weighted ensemble during the 1700s, but consistent with some UK stations (Fig. 12). Comparison of the annual MAM series with Pauling et al. (2006) reconstructions shows strong agreement with the weighted ensemble mean, with Jenkinson revealing lower annual totals prior to 1780 (Fig. 13).

In summer (JJA), both the Jenkinson and the weighted ensemble reconstructions show close agreement back to circa 1810. For earlier years, the Jenkinson series shows higher seasonal totals than the ensemble, especially in the upland domain. For extended summer (MJJA), better agreement is evident, but the Jenkinson series shows elevated totals relative to the ensemble for certain years in the latter half of the 18th-century. For autumn (SON), there is good agreement between the two approaches throughout the full reconstructions period.

Comparison of summer reconstructions with UK stations reveals that some UK stations (Spalding and Kew) show similarly high totals in the second half of the 1700s. Other stations (Oxford and Carlisle) tend to show closer correspondence to the weighted ensemble reconstructions in this period (Fig. 12). Comparison with Pauling et al. (2006) reconstructions for SA9 adds weight to the view that summer totals in this period may be overestimated in the Jenkinson reconstruction (Fig. 13). For MJJA the δ18O reconstruction tends to agree with the Jenkinson series during this period. However, divergence between the δ18O reconstruction and other observed and reconstructed series is evident in the early and late 1700s. The period 1730–1800 is subject to uncertainty about the extent and magnitude of wet summers. As the Jenkinson reconstruction during this period is strongly informed by Rutty's Dublin weather diary (1716–1765) and sparse early instrumental observations, it remains difficult to determine whether the elevated summer totals reflect genuine hydroclimatic conditions or uncertainties associated with documentary and early observational sources. This period is marked by notable spatial gradients in summer totals, which would be worth unpacking in future research. Autumn shows strong agreement across all reconstructions and available series throughout the record (Fig. 12).

Each monthly reconstruction was appended with observed rainfall from 1865 onwards, and annual and seasonal trends assessed for progressively later start years with a fixed end year of 2024 (Fig. 14). Both products contain identical observations from 1865 onwards, so their trends are identical for periods beginning in 1865 or later.

The largest differences in trends in both series occur in winter and spring. For tests beginning in the eighteenth century, Jenkinson generally indicates significant increases in DJF and MAM rainfall in both domains, whereas the ensemble indicates weak, non-significant decreases. The winter contrast is particularly pronounced in SA-upland. Annual rainfall also shows contrasting trend direction, magnitude and significance for tests commencing early in the record, with significant increases evident over many analysis periods in the SA-upland Jenkinson series but weak, non-significant decreases in the ensemble.

Both series indicate strong decreasing trends in JJA rainfall for tests commencing during their respective reconstruction periods. Decreasing trends are stronger and more persistently significant in the Jenkinson series, particularly in SA-upland. MJJA trends also generally indicate decreases for tests commencing throughout the reconstruction periods, although their significance depends on the domain and start year of the test. Autumn trends show the closest agreement between reconstructions, with trends commencing in the reconstruction period tending to show weak and non-significant trends relative to large and often significant increasing trends for tests commencing during the period of observations.