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The frequency change of 100‐year flood events is often determined by fitting extreme value distributions to annual maximum discharge from a historical base period. This study demonstrates that this approach may significantly bias the computed flood frequency change. An idealized experiment shows frequency bias exceeding 100% for a 50‐year base period. Further analyses using Monte Carlo simulations, mathematical derivations, and hydrological model outputs reveal that bias magnitude inversely relates to base period length and is weakly influenced by the generalized extreme value distribution's shape parameter. The bias, persisting across different estimation methods, implies floods may exceed local defenses designed based on short historical records more often than expected, even without climate change. We introduce a frequency bias adjustment method, which significantly reduces the projected rise in global flood occurrence. This suggests a substantial part of the earlier projected increase in flood occurrence and impacts is not attributable to climate change. Plain Language Summary: We report a previously overlooked bias in a common method for projecting the frequency of extreme floods, like 100‐year events. This method typically uses a short period of historical data for two primary purposes: first, to define local thresholds for extreme floods, such as 100‐year events, and second, to serve as a base for projecting future changes in flood occurrences. Our study, utilizing random data, demonstrates that this method can lead to inaccurate projections, increasing the number of expected extreme floods in future years and decreasing them in historical baseline years, even when no change is indicated by the data. Additionally, we find that the longer the historical period used, the smaller the bias. By simulating the characteristics of real hydrological model projections with artificial data, we are able to estimate a bias similar to that observed in these projections, and thus propose a simple method for its mitigation. Our findings indicate that previous studies might have overstated the impact of climate change on the projected increase in flood frequency and affected people. Additionally, using short historical records to design flood defenses can lead to more frequent flooding than anticipated, even without climate change. Key Points: Using a short historical period both to define extreme flood thresholds and as a base for future projections leads to significant biasThis bias shows an inverse relationship with the length of the base period and is slightly influenced by certain data characteristicsWe propose a practical adjustment method and suggestions to reduce biases in estimating future flood frequency changes [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events. Plain Language Summary: The El Niño Southern Oscillation (ENSO) is a climate phenomenon that involves changes in ocean temperatures in the central and eastern tropical Pacific and greatly influences the weather around the globe. ENSO events are usually categorized based on where these temperature variations are the strongest and how intense they are. However, these categories are at times ambiguous and the way ENSO behaves can change over decades. Our study introduces a new way to estimate how different kinds of ENSO events contribute to its overall variation over time. We base our variability analysis on a fuzzy categorization of ENSO events, which results in three different kinds of El Niño events and two kinds of La Niña events. We find that the decadal changes in ENSO variance, including the decadal shift in the mid‐1970s, were primarily associated with strong La Niña and extreme El Niño events. Key Points: Using a fuzzy categorization, five different El Niño Southern Oscillation (ENSO) types are identified in observationsExtreme El Niño events emerge as a distinct categoryStrong La Niña and Extreme El Niño categories mainly contributed to the shift in decadal ENSO variance around 1970 [ABSTRACT FROM AUTHOR]

Climatic observables are often correlated across long spatial distances, and extreme events, such as heatwaves or floods, are typically assumed to be related to such teleconnections.sup.1,2. Revealing atmospheric teleconnection patterns and understanding their underlying mechanisms is of great importance for weather forecasting in general and extreme-event prediction in particular.sup.3,4, especially considering that the characteristics of extreme events have been suggested to change under ongoing anthropogenic climate change.sup.5-8. Here we reveal the global coupling pattern of extreme-rainfall events by applying complex-network methodology to high-resolution satellite data and introducing a technique that corrects for multiple-comparison bias in functional networks. We find that the distance distribution of significant connections (P < 0.005) around the globe decays according to a power law up to distances of about 2,500 kilometres. For longer distances, the probability of significant connections is much higher than expected from the scaling of the power law. We attribute the shorter, power-law-distributed connections to regional weather systems. The longer, super-power-law-distributed connections form a global rainfall teleconnection pattern that is probably controlled by upper-level Rossby waves. We show that extreme-rainfall events in the monsoon systems of south-central Asia, east Asia and Africa are significantly synchronized. Moreover, we uncover concise links between south-central Asia and the European and North American extratropics, as well as the Southern Hemisphere extratropics. Analysis of the atmospheric conditions that lead to these teleconnections confirms Rossby waves as the physical mechanism underlying these global teleconnection patterns and emphasizes their crucial role in dynamical tropical-extratropical couplings. Our results provide insights into the function of Rossby waves in creating stable, global-scale dependencies of extreme-rainfall events, and into the potential predictability of associated natural hazards. Complex networks are used to analyse global-scale teleconnections between extreme-rainfall events, revealing a peak in the distance distribution of statistically significant connections at around 10,000 kilometres., Author(s): Niklas Boers [sup.1] [sup.2] , Bedartha Goswami [sup.2] , Aljoscha Rheinwalt [sup.3] , Bodo Bookhagen [sup.3] , Brian Hoskins [sup.1] [sup.4] , Jürgen Kurths [sup.2] [sup.5] [sup.6] Author Affiliations: [...]

The identification of recurrences at various time scales in extreme event-like time series is challenging because of the rare occurrence of events which are separated by large temporal gaps. Most of the existing time series analysis techniques cannot be used to analyse extreme event-like time series in its unaltered form. The study of the system dynamics by reconstruction of the phase space using the standard delay embedding method is not directly applicable to event-like time series as it assumes a Euclidean notion of distance between states in the phase space. The edit distance method is a novel approach that uses the point-process nature of events. We propose a modification of edit distance to analyze the dynamics of extreme event-like time series by incorporating a nonlinear function which takes into account the sparse distribution of extreme events and utilizes the physical significance of their temporal pattern. We apply the modified edit distance method to event-like data generated from point process as well as flood event series constructed from discharge data of the Mississippi River in USA, and compute their recurrence plots. From the recurrence analysis, we are able to quantify the deterministic properties of extreme event-like data. We also show that there is a significant serial dependency in the flood time series by using the random shuffle surrogate method. [ABSTRACT FROM AUTHOR]